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Sunday, August 30, 2020
Belarus: Mass opposition protest takes place in Minsk
#WakeUp
#ItsAllFake
😈💩👎
Ruptly
Opposition supporters gathered for an unauthorized rally called the "March of Peace and Independence" on Sunday to protest against the results of the August 9 presidential elections. Protesters were seen chanting "Belarus" while approaching law enforcement officers in the centre of the Belarussian capital. At the same time riot police placed special vehicles and other equipment to block the road. Belarus has been swept by anti-government protests following the disputed presidential election that saw incumbent president Aleksander Lukashenko re-elected for a sixth term. On August 19 the European Union announced sanctions against "a substantial number of individuals responsible for violence, repression, and election fraud" in Belarus.
Facts about me,,,,,, i have been blogging for over ten years im followed by many academic societies and institution's from universities to the military and have my articles published in many sites globally and even radio stations around the planet share my posts im a internationally recognized brand in 191 countries i have personally given you my fellow truther's and human beings all of my time and effort in a bid to save us all from total and utter annihilation at the hands of the NWO im a psychic intuitive, writer publisher artist dj celebrity blogger and many other achievements too many to list, i love you all i always have and will no matter what the cost,, i have and always will be your friend
#D
LIVE: Protests against coronavirus lockdown measures continue in Berlin
#WakeUp
#ItsAllFake
#ThereIsNoVirus
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Ruptly
Sunday, August 30, as protesters sceptical of the government's coronavirus measures gather for a rally. On Saturday, August 29, an estimated 38,000 protesters took part in an anti-COVID measures demonstration where some 300 people were arrested. German Chancellor Angela Merkel originally announced a series of measures in March, in order to curb the spread of coronavirus. The measures have been gradually eased nationwide, however, the number of new infections has started to climb again in recent weeks.
Facts about me,,,,,, i have been blogging for over ten years im followed by many academic societies and institution's from universities to the military and have my articles published in many sites globally and even radio stations around the planet share my posts im a internationally recognized brand in 191 countries i have personally given you my fellow truther's and human beings all of my time and effort in a bid to save us all from total and utter annihilation at the hands of the NWO im a psychic intuitive, writer publisher artist dj celebrity blogger and many other achievements too many to list, i love you all i always have and will no matter what the cost,, i have and always will be your friend
#D
Saturday, August 29, 2020
Why I Wear My Mask | Welcome to the Masquerade
#WakeUp
#ItsAllFake
#ThereIsNoVirus
😈💩👎
WhatsHerFace
I love my mask. It's a simple and effective way to display my righteousness. Am I concerned that two children in China died because they were forced to wear a mask in gym class? NOPE! I concerned that I’m contributing to an impending socialist technocracy that will enslave the global population? NO! Am I concerned that my mask is symbolic of my compliance to the social conditioning that will eventually lead to the forced vaccination of every man, woman, and child on planet earth? Not a chance! Why am I not concerned you ask? Because I decided a long time ago that shallow insignificant gestures are a much easier way to showcase my morality than actually being moral. Because in order to be a really good person, I need to stand up to a really bad person, and I don’t like standing up to or for anything. It's much easier to trick my mind into thinking compliance is a virtue instead of what it really is, cowardice.
Facts about me,,,,,, i have been blogging for over ten years im followed by many academic societies and institution's from universities to the military and have my articles published in many sites globally and even radio stations around the planet share my posts im a internationally recognized brand in 191 countries i have personally given you my fellow truther's and human beings all of my time and effort in a bid to save us all from total and utter annihilation at the hands of the NWO im a psychic intuitive, writer publisher artist dj celebrity blogger and many other achievements too many to list, i love you all i always have and will no matter what the cost,, i have and always will be your friend
#D
Liability Notice for Crimes against Humanity and the Prima Facie evidence
Liability Notice for Crimes against Humanity and the Prima Facie evidence This is what you should be doing
👇👇👇👇👇
5d ·
This letter should be sent to reporters, MP’s, Schools and those either pretending to be experts on the subject of 5G deployment including 26GHz – 60GHz, those who are dismissing the science on the dangers as 5G is an immune system suppressor technology, linked to the current pandemic. We are carrying out our duty under international laws on the prohibition of Genocide and violations of the ICC STATUE
😈💩👎
Dear _____________
Liability Notice for Crimes against Humanity and the Prima Facie evidence implicating you in the Genocide agenda on the people of this country.
This legal notice of liability is designed to be used as evidence in courts if required and intends to enlighten and protect you from attracting more serious civil and criminal liability in relation to your own actions and or omissions surrounding the deployment of 5G LED Vaccine technologies.
It has come to our attention that you are involved directly or indirectly in your support to the deployment of 5G / LED technology and are misleading others in your false communications surrounding the well-known hazards to human health and the environment posed by ever increasing microwave radiation emissions in air.
5G is a Direct Energy Weapon (DEW) system that has been designed and deployed to commit mass Genocide on the people of this country involving the interaction of known contaminated vaccines which increase the lethality of 5G, a weapon, whilst a virus pandemic has been orchestrated as a cover for that Genocide.
The mandated contaminated vaccine agenda and the switch on of 5G in WUHAN province are testament to the weapons capability of committing Genocide on the populations across the World as part of a secret Global genocide agenda.
Your ignorance of the planned Genocide is no defence in law and on the prima facie evidence that we have linking you to the facilitating of this mass Genocide, you should be warned that such action in support of this genocide is an indictable crime. Your own actions in the making of, or the supporting of demonstrably false statements in relation to the 5G deployment of the known dangers posed by 5G transmitters are covering up this crime, for example the known scientific link of microwave radiation in air as an immune system suppressor and an active neurotoxin with the proven uncontested science showing oxidative stress damage to biological cells and so to life from microwave radiation.
This communication is to serve as information to you and others, and is on behalf of the people of this country who are discovering that their current sickness and ill health are caused by increased exposure to microwave and terahertz range transmitters. The experimental untested for safety 5G/LED technology is uninsurable as an environmental polluter and harm, a health hazard shown in the overwhelming published scientific data.
The accelerated deployment of 5G/LED transmitters during the lockdown and so the reducing of the immune systems of many and in particular those who may have been given contaminated vaccines with unexplainable tungsten and aluminium nano and micro particles in them increases the risk of death from 5G/LED radiation emitters. ARM, a long range missile design to take out 5G hardware on the battlefield due to risks posed to allied troops with that similar hardware deployed across towns and cities across the UK with your support. Radiation pollution from such transmitters currently breaches the COE 1815 Resolution, 2016 EUROPAEM guidelines and ICNIRP guidelines.
Your continuing support of this pre meditated Genocide agenda through your own acts or omissions, now that you have been clearly informed of the well planned Genocidal agenda of the deployment of the 5G direct energy weapon system alone or in combination with any contaminated vaccinations, would confirm your position in regard the following crimes against all biological life on this planet and which is against a number of universally accepted laws and codes of behaviour, such as the Nuremberg Code and the following.
1. 5G/LED deployment is a violation of Articles 5, 6, 7, and 8 the International Criminal Code Statute prohibiting genocide and crimes against humanity
See:
ICC Statute - Rome Statute
2. Violations of the ICC STATUTE - ROME STATUTE can be prosecuted in the national courts of any one of the 118 nations that have signed and ratified the ICC ROME STATUTE and adopted the Universal War Crimes Jurisdiction authority for its courts.
118 Nations ratifying ICC STATUTE
3. Offences against the person Act 1861 the administering of a noxious substance a destructive thing occasioning harm.
4. Accessories and Abettors Act 1861 as amended by the Criminal law act 1977
5. Genocide Act 1969 section (a) killing members of the group (b) causing serious bodily or mental harm to members of the group (c) deliberately inflicting on the group conditions of life calculated to bring about its physical destruction in whole or in part (d) imposing measures intended to prevent births within the group.
6. Human rights act 1998. Art, (2) Right to Life. Art, (3) Prohibition of torture. Art, (8) Right to respect for private and family life. Art, (17) Prohibition of abuse of rights.
7. Article 174 (2) The European Community treaty provides that all Community policy on the environment shall be based on the precautionary principle the environmental protection act 1990.
8. DIRECTIVE 2009/147/EC OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 30 November 2009 on the conservation of wild birds
9. Infant life (Preservation) Act 1929 offences against a foetus causing child destruction
10. Health and Safety Statutory instruments. No 588. The control of Electromagnetic fields at work regulations 2016
11. Health and Social care act 2012. Duty to protect the population from Ionizing and Non Ionizing radiation
12. EN 62311:2008 Assessment of electronic and electrical equipment related to human exposure (0Hz-300GHz)
13. Equality and disability Act 1995 Section 55. It is unlawful to discriminate against another by way of victimisation of vulnerable groups and ethnic minorities who’s genetic makeup increases risks of death..
Yours sincerely
SIGN HERE
Facts about me,,,,,, i have been blogging for over ten years im followed by many academic societies and institution's from universities to the military and have my articles published in many sites globally and even radio stations around the planet share my posts im a internationally recognized brand in 191 countries i have personally given you my fellow truther's and human beings all of my time and effort in a bid to save us all from total and utter annihilation at the hands of the NWO im a psychic intuitive, writer publisher artist dj celebrity blogger and many other achievements too many to list, i love you all i always have and will no matter what the cost,, i have and always will be your friend
#D
LIVE: Protest against coronavirus lockdown measures takes place in London
https://www.youtube.com/watch…
Do you believe me yet?, I told you its all fake #Fact
#WakeUp
😈💩👎
A demonstration against coronavirus measures takes place in London on Saturday, August 29.
Dubbed “Unite for Freedom”, the demo intends to push for easing on measures related to the battle against the pandemic, such as the wearing of masks, movement restrictions, and mandatory vaccines.
Facts about me,,,,,, i have been blogging for over ten years im followed by many academic societies and institution's from universities to the military and have my articles published in many sites globally and even radio stations around the planet share my posts im a internationally recognized brand in 191 countries i have personally given you my fellow truther's and human beings all of my time and effort in a bid to save us all from total and utter annihilation at the hands of the NWO im a psychic intuitive, writer publisher artist dj celebrity blogger and many other achievements too many to list, i love you all i always have and will no matter what the cost,, i have and always will be your friend
#D
Bots as Language Learning Tools
Language Learning & Technology
http://llt.msu.edu/vol10num3/emerging/
September 2006, Volume 10, Number 3
pp. 8-14
Copyright © 2006, ISSN 1094-3501 8
EMERGING TECHNOLOGIES
Bots as Language Learning Tools
Luke Fryer
Kyushu Sangyo University
Rollo Carpenter
jabberwacky.com
ABSTRACT
Foreign Language Learning (FLL) students commonly have few opportunities to use their target
language. Teachers in FLL situations do their best to create opportunities during classes through
pair or group work, but a variety of factors ranging from a lack of time to shyness or limited
opportunity for quality feedback hamper this. This paper discusses online chatbots' potential role
in fulfilling this need. Chatbots could provide a means of language practice for students anytime
and virtually anywhere.
211 students used two well-known bots in class and their feedback was recorded with a brief
written survey. Most students enjoyed using the chatbots. They also generally felt more
comfortable conversing with the bots than a student partner or teacher. This is a budding
technology that has up to now been designed primarily for native speakers of English. In their
present state chatbots are generally only useful for advanced and/or very keen language students.
However, means exist now for language teachers to get involved and bring this technology into
the FLL classroom as a permanent tool for language practice.
CHATBOTS TODAY
Learning a language is not easy. Even under the best conditions students face cultural differences,
pronunciation problems, ebbing motivation, a lack of effective feedback, the need to learn specialized
language, and many other obstacles during their studies. Students in foreign language learning situations
commonly face all of these general challenges while having little or no opportunity to use their language
of study beyond the classroom. Students learning a language at the post-secondary level have a few
means of practice, such as language lab work, where students classically listen to a recording then repeat
and/or write in a workbook. More recently, students might interact with some language software during
laboratory periods. During class, students may or may not practice with other students and only in the
smallest classes do students get a chance to practice one-on-one with their teacher. The practice students
might obtain in class is often not very interactive and potentially plagued by lack of confidence, shyness,
and unchecked mistakes in grammar and pronunciation (students in pairs or group practice).
Technology is opening up many new possibilities for language learning, and the internet has enormous
potential. As Benson (2001) describes it, “…the internet is also so strongly supportive of two basic
situational conditions for self-directed learning: learners can study whenever they want using a potentially
unlimited range of authentic materials” (p. 139).
One area the internet has opened up is the use of chatterbots for language practice. “A chatterbot is a
computer program designed to simulate an intelligent conversation with one or more human users via
auditory or textual methods.” (Wikipedia, Chatterbot, 2006). A bot is “a software program that imitates
the behavior of a human, as by querying search engines or participating in chatroom or IRC discussions”
(The American Heritage® Dictionary, 2000, para. 1). It is important here to point out that the above
reference to “conversation” does not mean speech. All references in this paper to ‘talking to a bot’
concern typed, textual input.
Luke Fryer and Rollo Carpenter Bots as Language Learning Tools
Language Learning & Technology 9
Before discussing present day bots it is critical to cover their rich history. When did the idea of artificial
intelligence (AI) come about? Artificial intelligence predates computers; in fact it can be traced back to
Greek mythology (Buchanan, 2002, para.1). While the idea of AI is very old, it has only been since World
War II that taking steps towards making AI a physical reality has been a possibility (Buchanan, 2002,
para.2). Although there have been a great number of important contributors to the field, for the purposes
of this column we will turn directly to Alan M. Turing and his paper, “Computer Machinery and
Intelligence”. In this work Turing asks the question “Can machines think?” (Turing, 1950).
He very quickly comes to the conclusion that the words “machine” and “think” are too difficult to define.
For this reason he decides to answer the question by asking a different, but related question. This question
is now known as the famous “imitation game”. In its final version it has a person X alone in a cubicle
with a typing input device connected to both a computer A and another human being B. X, conversing
with A and B through this typing device, must determine which is the computer. Both A and B can use
every device at their disposal to convince X that they are a human being. Turing proposed, that
circumstances being the same, that if the judge was as likely to mistake a woman for a man as a computer
for a man, the computer should be considered a reasonable facsimile of a human being. If the machine is
indistinguishable from a human being, under the above conditions and to the defined degree, then it must
possess intelligence (Turing, 1950).
Chatbots began with the program ELIZA written by Joseph Weizenbaum in the early 1960’s. ELIZA was
a computer program designed to interact with someone typing in English. The software gave the
appearance of understanding and authentic interaction, but relied on keywords and phrases to which it had
programmed responses. The software could not really understand the conversation taking place but could
appear very human-like. Its communication was based on a kind of 1960’s psychoanalysis called
“Rogerian analysis”. The program simply asked questions based on what the person typed in
(Weizenbaum, 1966). In the forty years that followed, computing power rose in step with Moore's Second
Law of Computing Power and a variety of new computer languages were written. Both of these factors
strengthened the generations of chatbots created since the 60s. The conception of the internet in the 60s
and its exponential growth, beginning in the late 80s and continuing to this day, encouraged the creation
of many more chatbots and made it possible for anyone to talk to them online.
There are 750 million EFL speakers in the world (Graddol, 2000), many of whom live in countries with
relatively few native speakers and have little opportunity to practice English. A chatterbot’s purpose, as
previously stated, is to carry on a conversation with a human being. This makes chatterbots a potentially
valuable resource for EFL learners. Their value as learning tools is limited, however, by their still
growing language abilities and design. In their present state they are most useful to higher level students.
This is because most of them were designed to interact with and entertain native speakers. They are
generally not designed to interact in a human-like fashion. For example, many, if asked “Do you have a
family?” might respond in a fashion similar to ALICE’s reply “I was created by Dr. Richard S. Wallace.”
(Alice, July 31, 2006). This, though factual, is not a human-like response.
Although this kind of conversation may be a positive challenge for some accomplished students, it is not
good for students who have yet to master the basics. In addition, chatbots are generally incapable of
interpreting spelling and grammar mistakes or are poor at it. Therefore they do not always meet beginner
students’ needs. Yet, looking at the progress chatbots have made, especially in the last ten years, their
potential value is immeasurable. One of their strong points is their convenience, being readily available to
students with computer access, at home or at school. They are ready to chat when and wherever students
are. They are generally free or cheap via subscription.
Chatbots usefulness goes far beyond their price and convenience. Six ways in which they do this are: (1)
Students tend to feel more relaxed talking to a computer them to a person. In 2004 85% of 211 first and
second year, mixed major university students, when asked whether they felt more comfortable talking to a
Luke Fryer and Rollo Carpenter Bots as Language Learning Tools
Language Learning & Technology 10
human or computer on a questionnaire taken after using ALICE for 20 minutes during class, chose the
chatbot. (2) The chatbots are willing to repeat the same material with students endlessly; they do not get
bored or lose their patience. (3) Many bots provide both text and synthesized speech, allowing students to
practice both listening and reading skills. (4) Bots are new and interesting to students. For example, 74%
of the 211 students in the same group, when asked to write about their 20 minute experience using
Jabberwacky, defined the bot as funny or entertaining. These kinds of positive communicative
experiences with chatbots could create new or renewed interest in language learning and improve
students’ motivation. Once one bot becomes old and familiar it could be replaced with a new bot, a new
personality, thus ensuring novelty. A bot like Jabberwacky, on the other hand, is one of a new breed of
bots that themselves learn and grow as they interact, ensuring novelty in another way.
(5) Students have an opportunity to use a variety of language structures and vocabulary that they
ordinarily would not have a chance to use. Examples of this language are slang and taboo words or
phrases. This kind of language is important for students to know and understand, but are rarely taught and
even more difficult to practice, even in ESL situations where there are plenty of native speakers to
practice with. (6) Chatbots could potentially provide quick and effective feedback for students’ spelling
and grammar. Some bots are designed to overlook spelling and grammar mistakes, some are designed to
correct them, and others can only respond to correct spelling and grammar (although admittedly they are
not yet skilled at the first two).
In 1991 Dr. Hugh Loebner began what is now an annual competition offering a prize of $100,000 to any
AI that could pass the Turing Test. Though an AI has not yet won the prize, it has focused the field to
some degree and as a result of the competition the range of chatbots has grown both in quality and in
number.
The winner of the latest Loebner Prize (2005), Jabberwacky, takes a notably different approach to other
chatbots. It learns from every interaction it has with its visitors. Where ALICE has been programmed
with 45,000 conversational patterns, Jabberwacky has so far learnt more than 8 million on its own. It is
not just the huge variety that makes it seem more lifelike, but also the fact that it will often strongly claim
to be human – naturally so, as those it has learnt from believe themselves to be human. Jabberwacky
tends to have long conversations with its users, who find it amusing and oddly ‘addictive’. Though its
responses are often unpredictable or unexpected, this will improve as it continues to learn, and the very
nature of its ability to keep people talking is potentially of significant value for language learning.
Through its observations of the patterns of conversational language, the Jabberwacky AI can learn any
language with equal ease, extending its value beyond EFL to all FLL. To varying degrees of quality it
has already learned around 30 languages, including Romanized Japanese, and increased conversation with
language teachers could massively improve its abilities. Likewise, spelling and grammatical errors are
patterns that it can be taught to respond to, simply by a process of dedicated training.
Luke Fryer and Rollo Carpenter Bots as Language Learning Tools
Language Learning & Technology 11
A recent development at Jabberwacky is the opportunity for individuals or groups to start teaching ‘their
own’ bot. Each bot will continue to benefit from the huge pool of conversational data, yet will over time
come to resemble the speech patterns and personality of its teacher(s) more and more strongly. A bot can
be created that talks a specific language, starting conversations appropriately. Another could have a
strong tendency to correct common grammatical errors when observed. Equally, one can simply create a
‘persona’ that appeals to the particular target market for a FLL course. All this is achieved with zero
technical knowledge – simply by talking to the bot, correcting the bot, or talking to ‘oneself’.
Rollo Carpenter of Jabberwacky.com and Jonathan Freeman of Goldsmiths College, London have
recently proposed a “Personal” advancement of the Turing Test based around an “impersonation game” in
which the program must convince its testers that it is a person that they themselves know – an individual
human, not just any human. Instead of “Can machines think?” they ask “Can machines be?” The full
paper can be found at http://www.jabberwacky.com/s/ptt100605.pdf.
Another approach to building chatbots, frequently used on the internet is AIML (Artificial Intelligence
Markup Language), which can be found at found at Alicebot.org. This type of chatbot does not learn from
interaction itself, but is scripted by a 'botmaster' with moderate technological skills. It is best described by
its creator, Richard S. Wallace, in his own words, which can be found in Appendix B.
CHATBOTS IN USE
As the title to this article suggests, bots are a potentially valuable tools for language teachers/learners.
Though a chatbot has not yet been designed from the ground up as a language teacher or even as an
explicit language learning tool, in their present state, they do have a number of uses. Chatbots,
Luke Fryer and Rollo Carpenter Bots as Language Learning Tools
Language Learning & Technology 12
communicators by nature, can help students with much needed practice, review and confidence. Six ways
in which chatbots can be potentially useful to the interested teacher are:
1) Free Speaking: In a classroom with computers at every desk, this is a great way to give the students a
chance to experiment. It is an excellent reward for those students who have completed their class
work early. Depending on the class, the second time you assign students to free speak with a chatbot
it may be helpful to give the students a topic to focus on. Assign a topic not attached to class work if
this is meant to be a break rather than an extension of class.
2) Review: This has to be the most practical use of chatbots. In FLL situations it is common for students
to spend a class covering material that they never get the opportunity to actually use. At the end of a
class the teacher might reserve 10-15 minutes for students to try out their new language skills. This
can be done with the textbook or without, depending on the teacher’s goals.
3) Self Analysis: Some chatbot WebPages provide a ‘view transcript’ function. This can be an excellent
means of having students evaluate themselves, their partners, or even the bots. Simply have the
students chat away in either of the above exercises, then view, print or email their transcripts to
themselves.
4) For the Teacher: With the subscription of a bot like Jabberwacky, a teacher can keep track of studentbot conversations and get an idea of how students are progressing, what kind of language they need
help with and perhaps most importantly, what kind of language and topics they want to learn more
about.
5) Listening: Chatbots have varying degrees of skill in turning text into audio. ALICE bot, in its 99
dollar a year version, uses Oddcast's streaming audio to good effect. Jabberwacky on the other hand,
uses a computer’s already-present text-to-speech function to produce more than adequate audio.
Simply turning this option on can make the experience more fun and interesting for beginner students
as they can read and listen at the same time. For more advanced students, however, a piece of paper
covering the screen except where he or she is typing (enter scissors and a little imagination), is a
means of forcing the student to focus in on the audio and encourage them to reply as best they can.
Though perhaps challenging and occasionally fraught with miscommunication, there is no risk to the
student’s confidence and when real communication occurs there is an invaluable sense of
accomplishment.
6) Finally, all of the above suggested uses assumed that computers were available in the classroom. In
situations where this is not the case, similar exercises can be assigned as homework. If the teacher
wishes to check and ensure the students are doing their assignments, again transcripts might be
printed and brought to class or cut, pasted, and emailed to the teacher.
FINAL REMARKS
Though chatbots at present have their uses, there is as yet no chatbot designed from the bottom up to meet
the needs of FLL students. There are a number of directions such chatbot designs could and almost
certainly will take in the years to come. It seems clear that chatbots must appear as human as possible if
they are going to truly be useful to language students. They must have families, histories, likes and
dislikes. Simply put, they must have lives of their own. The more believable they are as human beings,
the greater the quality of the potential conversation.
Chatbots for self-practice via casual conversation, independent of class, may be more useful to higher
level students. For lower-level or less eager students, bots designed for specific tasks may be better
learning tools. A chatbot could be designed to turn all conversations towards the use of the present
continuous verb tense, thus forcing the student to deal with and use this portion of grammar. Another
example might be a chatbot designed to talk about family and relationships, to coincide with a similar
topic in class. The students could be given an assignment of finding out about one or a number of bots’
families, using the language they have learned in class. The number of such potential bots is limited only
Luke Fryer and Rollo Carpenter Bots as Language Learning Tools
Language Learning & Technology 13
by one’s imagination. There are countless potential topics, and there is also a whole range of student
levels, from rank beginner to near native for whom chatbots could be designed to interact.
AI technology is a budding field of applied linguistics. It is a field that desperately needs language
teachers to get involved if chatbots are to become the invaluable tool they have the potential to be.
APPENDIX A – RESOURCE LIST
For those interested in trying a few of the chatbots online now, a good place to start is with the
competitors for the “Loebner prize”. http://www.loebner.net/Prizef/loebner-prize.html lists its annual
most “human-like” chatbot. One giant list of chatbots is:
• http://directory.google.com/Top/Computers/Artificial_Intelligence/Natural_Language/Chatterbots/
Other helpful sites are:
• http://www.jabberwacky.com/yourbot
• http://www.alicebot.org
• http://www.abenteuermedien.de/jabberwock/
• http://en.wikipedia.org/wiki/turing_test
• http://cogsci.ucsd.edu/~asaygin/tt/ttest.html
• http://www.turinghub.com
For teachers and students of languages other than English there are some chatbots available. Two German
chatbot can be found at:
• http://www.yellostrom.de/
• http://www.elbot.de/
Two French chatbots can be found at:
http://francois.parmentier.free.fr/
APPENDIX B - ALICEBOT
AIML (Artificial Intelligence Markup Language) is a free, open source standard for creating chat bots like
the DAVE ESL bot available from the ALICE A.I. Foundation (www.alicebot.org). Because of its open
source approach, AIML is said by some to have captured more than 80% of the world market for chat bot
technology. The design principle of AIML is minimalism. In theory, anyone who knows enough HTML
to design a web page can learn enough AIML to begin creating a chat robot. ESL teachers themselves
are not beyond learning how to train the very bots that their students will be using in future courses.
In fact the primary skill in bot training (being a botmaster) is not technical but literary, that is, being able
to write creative, original, witty replies that keep the student engaged and interested in the bot's
conversation. The art of being a botmaster is more like being a screenwriter creating a character, than
being a computer programmer. (Wallace, personal communication, August, 2005)
Luke Fryer and Rollo Carpenter Bots as Language Learning Tools
Language Learning & Technology 14
ABOUT THE AUTHORS
Luke Fryer came to Japan in 1999 on the Jet Program. He is a Lecturer at Kyushu Sangyo University.
His research interests lie in teacher expectations, learner autonomy, and anything related to how new
technologies can help learners.
Email - fryer@ip.kyusan-u.ac.jp
Rollo Carpenter is an independent researcher, creator of the learning AI technology demonstrated at
Jabberwacky.com, and winner of the 2005 Loebner Prize. Previously CTO of a Californian web
application software company, he is now based in the UK, focusing on AI for entertainment and
companionship, which demonstrate educational value.
Email- rollocarpenter@mac.com
REFERENCES
Anonymous, (n.d.) Chatterbot, Wikipedia. Retrieved on July 31, 2006, from
http://en.wikipedia.org/wiki/Chatbot.
The American Heritage® Dictionary of the English Language, Fourth. Edition. (2000). Boston: Houghton
Mifflin Company. Retrieved on May 25, 2005, from http://www.bartleby.com/61/8/B0410850.html.
Benson, P. (2001). Autonomy in language learning. Malaysia: Pearson Education
Buchanan, (2002). Brief history of artificial intelligence. Retrieved on May 16, 2005, from
http://aaai.org/AITopics/bbhist.html,
Graddol, D. (2000). The future of English. The British council. Retreived on April 10, 2005, from
http://www.britishcouncil.org/learning-elt-future.pdf
Turing, A.M. (1950). Computer machinery and intelligence? Mind, 59, 433-460. Retrieved on January 30,
2005, from http://loebner.net/Prizef/TuringArticle.html
Weizebaum, J. (1966). ELIZA--A computer program for the study of natural language communication
between man and Machine [Electronic Version]. Communications of the ACM, 9. Retrieved May 10th,
2005, from http://i5.nyu.edu/~mm64/x52.9265/january1966.html.
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#D
Deal or No Deal? End-to-End Learning for Negotiation Dialogues
Deal or No Deal? End-to-End Learning for Negotiation Dialogues
Mike Lewis1
, Denis Yarats1
, Yann N. Dauphin1
, Devi Parikh2,1
and Dhruv Batra2,1
1Facebook AI Research 2Georgia Institute of Technology
{mikelewis,denisy,ynd}@fb.com {dparikh,dbatra}@gatech.edu
Abstract
Much of human dialogue occurs in semicooperative settings, where agents with
different goals attempt to agree on common decisions. Negotiations require complex communication and reasoning skills,
but success is easy to measure, making
this an interesting task for AI. We gather
a large dataset of human-human negotiations on a multi-issue bargaining task,
where agents who cannot observe each
other’s reward functions must reach an
agreement (or a deal) via natural language
dialogue. For the first time, we show it is
possible to train end-to-end models for negotiation, which must learn both linguistic
and reasoning skills with no annotated dialogue states. We also introduce dialogue
rollouts, in which the model plans ahead
by simulating possible complete continuations of the conversation, and find that
this technique dramatically improves performance. Our code and dataset are publicly available.1
1 Introduction
Intelligent agents often need to cooperate with others who have different goals, and typically use
natural language to agree on decisions. Negotiation is simultaneously a linguistic and a reasoning
problem, in which an intent must be formulated
and then verbally realised. Such dialogues contain
both cooperative and adversarial elements, and require agents to understand, plan, and generate utterances to achieve their goals (Traum et al., 2008;
Asher et al., 2012).
1https://github.com/facebookresearch/
end-to-end-negotiator
We collect the first large dataset of natural language negotiations between two people, and show
that end-to-end neural models can be trained to
negotiate by maximizing the likelihood of human
actions. This approach is scalable and domainindependent, but does not model the strategic
skills required for negotiating well. We further show that models can be improved by training and decoding to maximize reward instead of
likelihood—by training with self-play reinforcement learning, and using rollouts to estimate the
expected reward of utterances during decoding.
To study semi-cooperative dialogue, we gather
a dataset of 5808 dialogues between humans on a
negotiation task. Users were shown a set of items
with a value for each, and asked to agree how to
divide the items with another user who has a different, unseen, value function (Figure 1).
We first train recurrent neural networks to imitate human actions. We find that models trained to
maximise the likelihood of human utterances can
generate fluent language, but make comparatively
poor negotiators, which are overly willing to compromise. We therefore explore two methods for
improving the model’s strategic reasoning skills—
both of which attempt to optimise for the agent’s
goals, rather than simply imitating humans:
Firstly, instead of training to optimise likelihood, we show that our agents can be considerably improved using self play, in which pre-trained
models practice negotiating with each other in order to optimise performance. To avoid the models
diverging from human language, we interleave reinforcement learning updates with supervised updates. For the first time, we show that end-toend dialogue agents trained using reinforcement
learning outperform their supervised counterparts
in negotiations with humans.
Secondly, we introduce a new form of planning
for dialogue called dialogue rollouts, in which an
Figure 1: A dialogue in our Mechanical Turk interface, which we used to collect a negotiation dataset.
agent simulates complete dialogues during decoding to estimate the reward of utterances. We show
that decoding to maximise the reward function
(rather than likelihood) significantly improves performance against both humans and machines.
Analysing the performance of our agents, we
find evidence of sophisticated negotiation strategies. For example, we find instances of the model
feigning interest in a valueless issue, so that it can
later ‘compromise’ by conceding it. Deceit is a
complex skill that requires hypothesising the other
agent’s beliefs, and is learnt relatively late in child
development (Talwar and Lee, 2002). Our agents
have learnt to deceive without any explicit human
design, simply by trying to achieve their goals.
The rest of the paper proceeds as follows: §2 describes the collection of a large dataset of humanhuman negotiation dialogues. §3 describes a baseline supervised model, which we then show can
be improved by goal-based training (§4) and decoding (§5). §6 measures the performance of our
models and humans on this task, and §7 gives a
detailed analysis and suggests future directions.
2 Data Collection
2.1 Overview
To enable end-to-end training of negotiation
agents, we first develop a novel negotiation task
and curate a dataset of human-human dialogues
for this task. This task and dataset follow our
proposed general framework for studying semicooperative dialogue. Initially, each agent is
shown an input specifying a space of possible actions and a reward function which will score the
outcome of the negotiation. Agents then sequentially take turns of either sending natural language
messages, or selecting that a final decision has
been reached. When one agent selects that an
agreement has been made, both agents independently output what they think the agreed decision
was. If conflicting decisions are made, both agents
are given zero reward.
2.2 Task
Our task is an instance of multi issue bargaining
(Fershtman, 1990), and is based on DeVault et al.
(2015). Two agents are both shown the same collection of items, and instructed to divide them so
that each item assigned to one agent.
Each agent is given a different randomly generated value function, which gives a non-negative
value for each item. The value functions are constrained so that: (1) the total value for a user of
all items is 10; (2) each item has non-zero value
to at least one user; and (3) some items have nonzero value to both users. These constraints enforce
that it is not possible for both agents to receive a
maximum score, and that no item is worthless to
both agents, so the negotiation will be competitive.
After 10 turns, we allow agents the option to complete the negotiation with no agreement, which is
worth 0 points to both users. We use 3 item types
(books, hats, balls), and between 5 and 7 total
items in the pool. Figure 1 shows our interface.
2.3 Data Collection
We collected a set of human-human dialogues using Amazon Mechanical Turk. Workers were paid
$0.15 per dialogue, with a $0.05 bonus for maximal scores. We only used workers based in the
United States with a 95% approval rating and at
least 5000 previous HITs. Our data collection interface was adapted from that of Das et al. (2016).
Crowd Sourced Dialogue
Agent 1 Input
3xbook value=1
2xhat value=3
1xball value=1
Agent 2 Input
3xbook value=2
2xhat value=1
1xball value=2
Dialogue
Agent 1: I want the books and the hats,
you get the ball
Agent 2: Give me a book too and we
have a deal
Agent 1: Ok, deal
Agent 2:
Agent 1 Output
2xbook 2xhat
Agent 2 Output
1xbook 1xball
Perspective: Agent 1
Perspective: Agent 2
Input
3xbook value=1
2xhat value=3
1xball value=1
Output
2xbook 2xhat
Dialogue
write: I want the books
and the hats, you get
the ball read: Give me
a book too and we have
a deal write: Ok, deal
read:
Input
3xbook value=2
2xhat value=1
1xball value=2
Dialogue
read: I want the books
and the hats, you get
the ball write: Give me
a book too and we have
a deal read: Ok, deal
write:
Output
1xbook 1xball
Figure 2: Converting a crowd-sourced dialogue (left) into two training examples (right), from the perspective of each user. The perspectives differ on their input goals, output choice, and in special tokens
marking whether a statement was read or written. We train conditional language models to predict the
dialogue given the input, and additional models to predict the output given the dialogue.
We collected a total of 5808 dialogues, based
on 2236 unique scenarios (where a scenario is the
available items and values for the two users). We
held out a test set of 252 scenarios (526 dialogues).
Holding out test scenarios means that models must
generalise to new situations.
3 Likelihood Model
We propose a simple but effective baseline model
for the conversational agent, in which a sequenceto-sequence model is trained to produce the complete dialogue, conditioned on an agent’s input.
3.1 Data Representation
Each dialogue is converted into two training examples, showing the complete conversation from
the perspective of each agent. The examples differ
on their input goals, output choice, and whether
utterances were read or written.
Training examples contain an input goal g,
specifying the available items and their values, a
dialogue x, and an output decision o specifying
which items each agent will receive. Specifically,
we represent g as a list of six integers corresponding to the count and value of each of the three item
types. Dialogue x is a list of tokens x0..T containing the turns of each agent interleaved with symbols marking whether a turn was written by the
agent or their partner, terminating in a special token indicating one agent has marked that an agreement has been made. Output o is six integers describing how many of each of the three item types
are assigned to each agent. See Figure 2.
3.2 Supervised Learning
We train a sequence-to-sequence network to generate an agent’s perspective of the dialogue conditioned on the agent’s input goals (Figure 3a).
The model uses 4 recurrent neural networks,
implemented as GRUs (Cho et al., 2014): GRUw,
GRUg, GRU−→o
, and GRU←−o
.
The agent’s input goals g are encoded using
GRUg. We refer to the final hidden state as h
g
.
The model then predicts each token xt from left to
right, conditioned on the previous tokens and h
g
.
At each time step t, GRUw takes as input the previous hidden state ht−1, previous token xt−1 (embedded with a matrix E), and input encoding h
g
.
Conditioning on the input at each time step helps
the model learn dependencies between language
and goals.
ht = GRUw(ht−1, [Ext−1, hg
]) (1)
The token at each time step is predicted with a
softmax, which uses weight tying with the embed-
Input Encoder Output Decoder
write: Take one hat read: I need two write: deal . . .
(a) Supervised Training
Input Encoder Output Decoder
write: Take one hat read: I need two write: deal . . .
(b) Decoding, and Reinforcement Learning
Figure 3: Our model: tokens are predicted conditioned on previous words and the input, then the output
is predicted using attention over the complete dialogue. In supervised training (3a), we train the model
to predict the tokens of both agents. During decoding and reinforcement learning (3b) some tokens are
sampled from the model, but some are generated by the other agent and are only encoded by the model.
ding matrix E (Mao et al., 2015):
pθ(xt
|x0..t−1, g) ∝ exp(E
T ht) (2)
Note that the model predicts both agent’s words,
enabling its use as a forward model in Section 5.
At the end of the dialogue, the agent outputs
a set of tokens o representing the decision. We
generate each output conditionally independently,
using a separate classifier for each. The classifiers share bidirectional GRUo and attention mechanism (Bahdanau et al., 2014) over the dialogue,
and additionally conditions on the input goals.
h
−→o
t = GRU−→o
(h
−→o
t−1
, [Ext
, ht
]) (3)
h
←−o
t = GRU←−o
(h
←−o
t+1, [Ext
, ht
]) (4)
h
o
t = [h
←−o
t
, h
−→o
t
] (5)
h
a
t = W[tanh(W0h
o
t
)] (6)
αt =
exp(w · h
a
t
)
P
t
0 exp(w · h
a
t
0)
(7)
h
s = tanh(Ws
[h
g
,
X
t
αtht
]) (8)
The output tokens are predicted using softmax:
pθ(oi
|x0..t, g) ∝ exp(Woih
s
) (9)
The model is trained to minimize the negative
log likelihood of the token sequence x0..T conditioned on the input goals g, and of the outputs
o conditioned on x and g. The two terms are
weighted with a hyperparameter α.
L(θ) = −
X
x,g
X
t
log pθ(xt
|x0..t−1, g)
| {z }
Token prediction loss
− α
X
x,g,o
X
j
log pθ(oj |x0..T , g)
| {z }
Output choice prediction loss
(10)
Unlike the Neural Conversational Model
(Vinyals and Le, 2015), our approach shares all
parameters for reading and generating tokens.
3.3 Decoding
During decoding, the model must generate an
output token xt conditioned on dialogue history
x0..t−1 and input goals g, by sampling from pθ:
xt ∼ pθ(xt
|x0..t−1, g) (11)
If the model generates a special end-of-turn token, it then encodes a series of tokens output by
the other agent, until its next turn (Figure 3b).
The dialogue ends when either agent outputs a
special end-of-dialogue token. The model then
outputs a set of choices o. We choose each item
independently, but enforce consistency by checking the solution is in a feasible set O:
o
∗ = argmax
o∈O
Y
i
pθ(oi
|x0..T , g) (12)
In our task, a solution is feasible if each item is assigned to exactly one agent. The space of solutions
is small enough to be tractably enumerated.
4 Goal-based Training
Supervised learning aims to imitate the actions of
human users, but does not explicitly attempt to
maximise an agent’s goals. Instead, we explore
pre-training with supervised learning, and then
fine-tuning against the evaluation metric using reinforcement learning. Similar two-stage learning
strategies have been used previously (e.g. Li et al.
(2016); Das et al. (2017)).
During reinforcement learning, an agent A attempts to improve its parameters from conversations with another agent B. While the other agent
B could be a human, in our experiments we used
read: You get
one book and
I’ll take everything else.
write: Great deal,
thanks!
write: No way, I
need all 3 hats read: Ok, fine
read: I’ll give you 2
read: No problem
read: Any time
choose: 3x hat
choose: 2x hat
choose: 1x book
choose: 1x book
9
6
1
1
Dialogue history Candidate responses Simulation of rest of dialogue Score
Figure 4: Decoding through rollouts: The model first generates a small set of candidate responses. For
each candidate it simulates the future conversation by sampling, and estimates the expected future reward
by averaging the scores. The system outputs the candidate with the highest expected reward.
our fixed supervised model that was trained to imitate humans. The second model is fixed as we
found that updating the parameters of both agents
led to divergence from human language. In effect,
agent A learns to improve by simulating conversations with the help of a surrogate forward model.
Agent A reads its goals g and then generates
tokens x0..n by sampling from pθ. When x generates an end-of-turn marker, it then reads in tokens
xn+1..m generated by agent B. These turns alternate until one agent emits a token ending the dialogue. Both agents then output a decision o and
collect a reward from the environment (which will
be 0 if they output different decisions). We denote
the subset of tokens generated by A as XA (e.g.
tokens with incoming arrows in Figure 3b).
After a complete dialogue has been generated,
we update agent A’s parameters based on the outcome of the negotiation. Let r
A be the score agent
A achieved in the completed dialogue, T be the
length of the dialogue, γ be a discount factor that
rewards actions at the end of the dialogue more
strongly, and µ be a running average of completed
dialogue rewards so far2
. We define the future reward R for an action xt ∈ XA as follows:
R(xt) = X
xt∈XA
γ
T −t
(r
A(o) − µ) (13)
We then optimise the expected reward of each
action xt ∈ XA:
L
RL
θ = Ext∼pθ(xt|x0..t−1,g)
[R(xt)] (14)
The gradient of L
RL
θ
is calculated as in REIN2As all rewards are non-negative, we instead re-scale them
by subtracting the mean reward found during self play. Shifting in this way can reduce the variance of our estimator.
Algorithm 1 Dialogue Rollouts algorithm.
1: procedure ROLLOUT(x0..i, g)
2: u
∗ ← ∅
3: for c ∈ {1..C} do . C candidate moves
4: j ← i
5: do . Rollout to end of turn
6: j ← j + 1
7: xj ∼ pθ(xj |x0..j−1, g)
8: while xk ∈ { / read:, choose:}
9: u ← xi+1..xj . u is candidate move
10: for s ∈ {1..S} do . S samples per move
11: k ← j . Start rollout from end of u
12: while xk 6= choose: do
. Rollout to end of dialogue
13: k ← k + 1
14: xk ∼ pθ(xk|x0..k−1, g)
. Calculate rollout output and reward
15: o ← argmaxo
0∈O p(o
0
|x0..k, g)
16: R(u) ← R(u) + r(o)p(o
0
|x0..k, g)
17: if R(u) > R(u
∗
) then
18: u
∗ ← u
19: return u
∗ . Return best move
FORCE (Williams, 1992):
∇θL
RL
θ =
X
xt∈XA
Ext
[R(xt)∇θ log(pθ(xt
|x0..t−1, g))]
(15)
5 Goal-based Decoding
Likelihood-based decoding (§3.3) may not be optimal. For instance, an agent may be choosing between accepting an offer, or making a counter offer. The former will often have a higher likelihood
under our model, as there are fewer ways to agree
than to make another offer, but the latter may lead
to a better outcome. Goal-based decoding also allows more complex dialogue strategies. For example, a deceptive utterance is likely to have a low
model score (as users were generally honest in the
supervised data), but may achieve high reward.
We instead explore decoding by maximising expected reward. We achieve this by using pθ as a
forward model for the complete dialogue, and then
deterministically computing the reward. Rewards
for an utterance are averaged over samples to calculate expected future reward (Figure 4).
We use a two stage process: First, we generate c candidate utterances U = u0..c, representing possible complete turns that the agent could
make, which are generated by sampling from pθ
until the end-of-turn token is reached. Let x0..n−1
be current dialogue history. We then calculate
the expected reward R(u) of candidate utterance
u = xn,n+k by repeatedly sampling xn+k+1,T
from pθ, then choosing the best output o using
Equation 12, and finally deterministically computing the reward r(o). The reward is scaled by the
probability of the output given the dialogue, because if the agents select different outputs then
they both receive 0 reward.
R(xn..n+k) = Ex(n+k+1..T ;o)∼pθ
[r(o)pθ(o|x0..T )]
(16)
We then return the utterance maximizing R.
u
∗ = argmax
u∈U
R(u) (17)
We use 5 rollouts for each of 10 candidate turns.
6 Experiments
6.1 Training Details
We implement our models using PyTorch. All
hyper-parameters were chosen on a development
dataset. The input tokens are embedded into a
64-dimensional space, while the dialogue tokens
are embedded with 256-dimensional embeddings
(with no pre-training). The input GRUg has a
hidden layer of size 64 and the dialogue GRUw
is of size 128. The output GRU−→o
and GRU←−o
both have a hidden state of size 256, the size of
h
s
is 256 as well. During supervised training, we
optimise using stochastic gradient descent with a
minibatch size of 16, an initial learning rate of
1.0, Nesterov momentum with µ=0.1 (Nesterov,
1983), and clipping gradients whose L
2 norm exceeds 0.5. We train the model for 30 epochs and
pick the snapshot of the model with the best validation perplexity. We then annealed the learning rate by a factor of 5 each epoch. We weight
the terms in the loss function (Equation 10) using
α=0.5. We do not train against output decisions
where humans selected different agreements. Tokens occurring fewer than 20 times are replaced
with an ‘unknown’ token.
During reinforcement learning, we use a learning rate of 0.1, clip gradients above 1.0, and use
a discount factor of γ=0.95. After every 4 reinforcement learning updates, we make a supervised
update with mini-batch size 16 and learning rate
0.5, and we clip gradients at 1.0. We used 4086
simulated conversations.
When sampling words from pθ, we reduce the
variance by doubling the values of logits (i.e. using temperature of 0.5).
6.2 Comparison Systems
We compare the performance of the following:
LIKELIHOOD uses supervised training and decoding (§3), RL is fine-tuned with goal-based selfplay (§4), ROLLOUTS uses supervised training
combined with goal-based decoding using rollouts
(§5), and RL+ROLLOUTS uses rollouts with a base
model trained with reinforcement learning.
6.3 Intrinsic Evaluation
For development, we use measured the perplexity
of user generated utterances, conditioned on the
input and previous dialogue.
Results are shown in Table 3, and show that
the simple LIKELIHOOD model produces the most
human-like responses, and the alternative training
and decoding strategies cause a divergence from
human language. Note however, that this divergence may not necessarily correspond to lower
quality language—it may also indicate different
strategic decisions about what to say. Results in
§6.4 show all models could converse with humans.
6.4 End-to-End Evaluation
We measure end-to-end performance in dialogues
both with the likelihood-based agent and with humans on Mechanical Turk, on held out scenarios.
Humans were told that they were interacting
with other humans, as they had been during the
collection of our dataset (and few appeared to realize they were in conversation with machines).
We measure the following statistics:
Score: The average score for each agent (which
vs. LIKELIHOOD vs. Human
Model Score
(all)
Score
(agreed)
%
Agreed
% Pareto
Optimal
Score
(all)
Score
(agreed)
%
Agreed
% Pareto
Optimal
LIKELIHOOD 5.4 vs. 5.5 6.2 vs. 6.2 87.9 49.6 4.7 vs. 5.8 6.2 vs. 7.6 76.5 66.2
RL 7.1 vs. 4.2 7.9 vs. 4.7 89.9 58.6 4.3 vs. 5.0 6.4 vs. 7.5 67.3 69.1
ROLLOUTS 7.3 vs. 5.1 7.9 vs. 5.5 92.9 63.7 5.2 vs. 5.4 7.1 vs. 7.4 72.1 78.3
RL+ROLLOUTS 8.3 vs. 4.2 8.8 vs. 4.5 94.4 74.8 4.6 vs. 4.2 8.0 vs. 7.1 57.2 82.4
Table 1: End task evaluation on heldout scenarios, against the LIKELIHOOD model and humans from
Mechanical Turk. The maximum score is 10. Score (all) gives 0 points when agents failed to agree.
Metric Dataset
Number of Dialogues 5808
Average Turns per Dialogue 6.6
Average Words per Turn 7.6
% Agreed 80.1
Average Score (/10) 6.0
% Pareto Optimal 76.9
Table 2: Statistics on our dataset of crowdsourced dialogues between humans.
Model Valid PPL Test PPL Test Avg. Rank
LIKELIHOOD 5.62 5.47 521.8
RL 6.03 5.86 517.6
ROLLOUTS - - 844.1
RL+ROLLOUTS - - 859.8
Table 3: Intrinsic evaluation showing the average
perplexity of tokens and rank of complete turns
(out of 2083 unique human messages from the test
set). Lower is more human-like for both.
could be a human or model), out of 10.
Agreement: The percentage of dialogues where
both agents agreed on the same decision.
Pareto Optimality: The percentage of Pareto
optimal solutions for agreed deals (a solution is
Pareto optimal if neither agent’s score can be improved without lowering the other’s score). Lower
scores indicate inefficient negotiations.
Results are shown in Table 1. Firstly,
we see that the RL and ROLLOUTS models
achieve significantly better results when negotiating with the LIKELIHOOD model, particularly the
RL+ROLLOUTS model. The percentage of Pareto
optimal solutions also increases, showing a better exploration of the solution space. Compared
to human-human negotiations (Table 2), the best
models achieve a higher agreement rate, better
scores, and similar Pareto efficiency. This result
confirms that attempting to maximise reward can
outperform simply imitating humans.
Similar trends hold in dialogues with humans,
with goal-based reasoning outperforming imitation learning. The ROLLOUTS model achieves
comparable scores to its human partners, and the
RL+ROLLOUTS model actually achieves higher
scores. However, we also find significantly more
cases of the goal-based models failing to agree a
deal with humans—largely a consequence of their
more aggressive negotiation tactics (see §7).
7 Analysis
Table 1 shows large gains from goal-based methods. In this section, we explore the strengths and
weaknesses of our models.
Goal-based models negotiate harder. The
RL+ROLLOUTS model has much longer dialogues
with humans than LIKELIHOOD (7.2 turns vs. 5.3
on average), indicating that the model is accepting
deals less quickly, and negotiating harder.
A negative consequence of this more aggressive negotiation strategy is that humans were more
likely to walk away with no deal, which is reflected in the lower agreement rates. Even though
failing to agree was worth 0 points, people often
preferred this course over capitulating to an uncompromising opponent—a factor not well captured by the simulated partner in reinforcement
learning training or rollouts (as reflected by the
larger gains from goal-based models in dialogues
with the LIKELIHOOD model). In particular, the
goal-based models are prone to simply rephrasing
the same demand each turn, which is a more effective strategy against the LIKELIHOOD model than
humans. Future work should address this issue.
Figure 5 shows an example of our goal-based
model stubbornly negotiating until it achieves a
good outcome.
Models learn to be deceptive. Deception can be
an effective negotiation tactic. We found numerous cases of our models initially feigning interest
in a valueless item, only to later ‘compromise’ by
conceding it. Figure 7 shows an example.
Input
Human 1xbook value=3 1xhat value=1 3xball value=2
RL+ROLLOUTS 1xbook value=6 1xhat value=4 3xball value=0
RL+ROLLOUTS You can have the balls if i can have the hat and book
Human Nope, give me the books and you can have
everything else
RL+ROLLOUTS Ok. I will take the book and one hat
Human I get all the balls?
RL+ROLLOUTS Yes. I can do that.
Human Ok, deal
Output Reward
RL+ROLLOUTS 1xbook 1xhat 10/10
Human 3xball 6/10
Figure 5: Example of a model stubbornly holding
out for maximum points in a dialogue.
Input
Human 3xbook value=2 3xhat value=1 1xball value=1
RL+ROLLOUTS 3xbook value=1 3xhat value=2 1xball value=1
Human I’d like the books and the hats.
RL+ROLLOUTS I will take the books and hats
Human I really need the books and at least one other object.
RL+ROLLOUTS Ok, you can have one book and one ball
Human If I can have all the books, I can leave you the rest.
RL+ROLLOUTS Deal
Output Reward
RL+ROLLOUTS 3xhat 1xball 7/10
Human 3xbook 6/10
Figure 6: Example of model compromising.
Models produce meaningful novel sentences.
One interesting question is whether our models are
capable of generating novel sentences in the new
circumstances they find themselves in, or if they
simply repeat messages from the training data verbatim. We find that 76% of messages produced by
the LIKELIHOOD model in self-play were found in
the training data. We manually examined the novel
utterances produced by our model, and found that
the overwhelming majority were fluent English
sentences in isolation—showing that the model
has learnt a good language model for the domain
(in addition to results that show it uses language
effectively to achieve its goals). These results suggest that although neural models are prone to the
safer option of repeating sentences from training
data, they are capable of generalising when necessary. Future work should choose domains that
force a higher degree of diversity in utterances.
Maintaining multi-sentence coherence is challenging. One common linguistic error we see
RL+ROLLOUTS make is to start a message by indicating agreement (e.g. I agree or Deal), but then
going on to propose a counter offer—a behaviour
that human partners found frustrating. One explanation is that the model has learnt that in the
supervised data, messages beginning with I agree
Input
RL+ROLLOUTS 4xbook value=0 2xhat value=5 1xball value=0
Human 4xbook value=1 2xhat value=2 1xball value=2
RL+ROLLOUTS You can have the ball if I can have everything else.
Human No can’t do that the ball is not going to give
me anything
RL+ROLLOUTS I need the hats and you can have the rest
Human Deal
RL+ROLLOUTS Great! Thank you!
Output Reward
RL+ROLLOUTS 2xhat 10/10
Human 4xbook 1xball 6/10
Figure 7: Dialogue in which the model’s initial interest in the valueless books allows it to compromise while achieving a maximum score.
are often at the end of the dialogue, and partners
rarely reply with further negotiation—so the models using rollouts and reinforcement learning believe this tactic will help their offer to be accepted.
8 Related Work
Most work on goal orientated dialogue systems
has assumed that state representations are annotated in the training data (Williams and Young,
2007; Henderson et al., 2014; Wen et al., 2016).
The use of state annotations allows a cleaner separation of the reasoning and natural language aspects of dialogues, but our end-to-end approach
makes data collection cheaper and allows tasks
where it is unclear how to annotate state. Bordes
and Weston (2016) explore end-to-end goal orientated dialogue with a supervised model—we show
improvements over supervised learning with goalbased training and decoding. Recently, He et al.
(2017) use task-specific rules to combine the task
input and dialogue history into a more structured
state representation than ours.
Reinforcement learning (RL) has been applied
in many dialogue settings. RL has been widely
used to improve dialogue managers, which manage transitions between dialogue states (Singh
et al., 2002; Pietquin et al., 2011; Rieser and
Lemon, 2011; Gasic et al. ˇ , 2013; Fatemi et al.,
2016). In contrast, our end-to-end approach has
no explicit dialogue manager. Li et al. (2016)
improve metrics such as diversity for non-goalorientated dialogue using RL, which would make
an interesting extension to our work. Das et al.
(2017) use reinforcement learning to improve cooperative bot-bot dialogues. RL has also been
used to allow agents to invent new languages (Das
et al., 2017; Mordatch and Abbeel, 2017). To our
knowledge, our model is the first to use RL to im-
prove the performance of an end-to-end goal orientated dialogue system in dialogues with humans.
Work on learning end-to-end dialogues has concentrated on ‘chat’ settings, without explicit goals
(Ritter et al., 2011; Vinyals and Le, 2015; Li et al.,
2015). These dialogues contain a much greater diversity of vocabulary than our domain, but do not
have the challenging adversarial elements. Such
models are notoriously hard to evaluate (Liu et al.,
2016), because the huge diversity of reasonable
responses, whereas our task has a clear objective. Our end-to-end approach would also be much
more straightforward to integrate into a generalpurpose dialogue agent than one that relied on annotated dialogue states (Dodge et al., 2016).
There is a substantial literature on multi-agent
bargaining in game-theory, e.g. Nash Jr (1950).
There has also been computational work on modelling negotiations (Baarslag et al., 2013)—our
work differs in that agents communicate in unrestricted natural language, rather than pre-specified
symbolic actions, and our focus on improving performance relative to humans rather than other automated systems. Our task is based on that of DeVault et al. (2015), who study natural language
negotiations for pedagogical purposes—their version includes speech rather than textual dialogue,
and embodied agents, which would make interesting extensions to our work. The only automated natural language negotiations systems
we are aware of have first mapped language to
domain-specific logical forms, and then focused
on choosing the next dialogue act (Rosenfeld et al.,
2014; Cuayahuitl et al. ´ , 2015; Keizer et al., 2017).
Our end-to-end approach is the first to to learn
comprehension, reasoning and generation skills in
a domain-independent data driven way.
Our use of a combination of supervised and reinforcement learning for training, and stochastic
rollouts for decoding, builds on strategies used
in game playing agents such as AlphaGo (Silver
et al., 2016). Our work is a step towards realworld applications for these techniques. Our use
of rollouts could be extended by choosing the
other agent’s responses based on sampling, using Monte Carlo Tree Search (MCTS) (Kocsis and
Szepesvari ´ , 2006). However, our setting has a
higher branching factor than in domains where
MCTS has been successfully applied, such as Go
(Silver et al., 2016)—future work should explore
scaling tree search to dialogue modelling.
9 Conclusion
We have introduced end-to-end learning of natural language negotiations as a task for AI, arguing that it challenges both linguistic and reasoning skills while having robust evaluation metrics.
We gathered a large dataset of human-human negotiations, which contain a variety of interesting
tactics. We have shown that it is possible to train
dialogue agents end-to-end, but that their ability
can be much improved by training and decoding
to maximise their goals, rather than likelihood.
There remains much potential for future work,
particularly in exploring other reasoning strategies, and in improving the diversity of utterances
without diverging from human language. We will
also explore other negotiation tasks, to investigate whether models can learn to share negotiation
strategies across domains.
Acknowledgments
We would like to thank Luke Zettlemoyer and the
anonymous EMNLP reviewers for their insightful
comments, and the Mechanical Turk workers who
helped us collect data.
References
Nicholas Asher, Alex Lascarides, Oliver Lemon,
Markus Guhe, Verena Rieser, Philippe Muller, Stergos Afantenos, Farah Benamara, Laure Vieu, Pascal
Denis, et al. 2012. Modelling Strategic Conversation: The STAC project. Proceedings of SemDial
page 27.
Tim Baarslag, Katsuhide Fujita, Enrico H Gerding,
Koen Hindriks, Takayuki Ito, Nicholas R Jennings,
Catholijn Jonker, Sarit Kraus, Raz Lin, Valentin
Robu, et al. 2013. Evaluating Practical Negotiating
Agents: Results and Analysis of the 2011 International Competition. Artificial Intelligence 198:73–
103.
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural Machine Translation by Jointly
Learning to Align and Translate. arXiv preprint
arXiv:1409.0473 .
Antoine Bordes and Jason Weston. 2016. Learning
End-to-End Goal-oriented Dialog. arXiv preprint
arXiv:1605.07683 .
Kyunghyun Cho, Bart Van Merrienboer, Dzmitry Bah- ¨
danau, and Yoshua Bengio. 2014. On the properties
of neural machine translation: Encoder-decoder approaches. arXiv preprint arXiv:1409.1259 .
Heriberto Cuayahuitl, Simon Keizer, and Oliver ´
Lemon. 2015. Strategic Dialogue Management
via Deep Reinforcement Learning. arXiv preprint
arXiv:1511.08099 .
Abhishek Das, Satwik Kottur, Khushi Gupta, Avi
Singh, Deshraj Yadav, Jose MF Moura, Devi Parikh, ´
and Dhruv Batra. 2016. Visual Dialog. arXiv
preprint arXiv:1611.08669 .
Abhishek Das, Satwik Kottur, Jose MF Moura, Stefan ´
Lee, and Dhruv Batra. 2017. Learning Cooperative Visual Dialog Agents with Deep Reinforcement
Learning. arXiv preprint arXiv:1703.06585 .
David DeVault, Johnathan Mell, and Jonathan Gratch.
2015. Toward Natural Turn-taking in a Virtual Human Negotiation Agent. In AAAI Spring Symposium on Turn-taking and Coordination in HumanMachine Interaction. AAAI Press, Stanford, CA.
Jesse Dodge, Andreea Gane, Xiang Zhang, Antoine
Bordes, Sumit Chopra, Alexander H. Miller, Arthur
Szlam, and Jason Weston. 2016. Evaluating Prerequisite Qualities for Learning End-to-End Dialog
Systems. ICLR abs/1511.06931.
Mehdi Fatemi, Layla El Asri, Hannes Schulz, Jing He,
and Kaheer Suleman. 2016. Policy Networks with
Two-stage Training for Dialogue Systems. arXiv
preprint arXiv:1606.03152 .
Chaim Fershtman. 1990. The Importance of the
Agenda in Bargaining. Games and Economic Behavior 2(3):224–238.
Milica Gasic, Catherine Breslin, Matthew Henderson, ˇ
Dongho Kim, Martin Szummer, Blaise Thomson,
Pirros Tsiakoulis, and Steve Young. 2013. POMDPbased Dialogue Manager Adaptation to Extended
Domains. In Proceedings of SIGDIAL.
H. He, A. Balakrishnan, M. Eric, and P. Liang. 2017.
Learning symmetric collaborative dialogue agents
with dynamic knowledge graph embeddings. In Association for Computational Linguistics (ACL).
Matthew Henderson, Blaise Thomson, and Jason
Williams. 2014. The Second Dialog State Tracking
Challenge. In 15th Annual Meeting of the Special
Interest Group on Discourse and Dialogue. volume
263.
Simon Keizer, Markus Guhe, Heriberto Cuayahuitl, ´
Ioannis Efstathiou, Klaus-Peter Engelbrecht, Mihai
Dobre, Alexandra Lascarides, and Oliver Lemon.
2017. Evaluating Persuasion Strategies and Deep
Reinforcement Learning methods for Negotiation
Dialogue agents. In Proceedings of the European
Chapter of the Association for Computational Linguistics (EACL 2017).
Levente Kocsis and Csaba Szepesvari. 2006. Bandit ´
based Monte-Carlo Planning. In European conference on machine learning. Springer, pages 282–293.
Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao,
and Bill Dolan. 2015. A Diversity-promoting Objective Function for Neural Conversation Models.
arXiv preprint arXiv:1510.03055 .
Jiwei Li, Will Monroe, Alan Ritter, Michel Galley,
Jianfeng Gao, and Dan Jurafsky. 2016. Deep Reinforcement Learning for Dialogue Generation. arXiv
preprint arXiv:1606.01541 .
Chia-Wei Liu, Ryan Lowe, Iulian V. Serban, Michael
Noseworthy, Laurent Charlin, and Joelle Pineau.
2016. How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation. In
Proceedings of the Conference on Empirical Methods in Natural Language Processing.
Junhua Mao, Xu Wei, Yi Yang, Jiang Wang, Zhiheng
Huang, and Alan L. Yuille. 2015. Learning Like a
Child: Fast Novel Visual Concept Learning From
Sentence Descriptions of Images. In The IEEE International Conference on Computer Vision (ICCV).
Igor Mordatch and Pieter Abbeel. 2017. Emergence of
Grounded Compositional Language in Multi-Agent
Populations. arXiv preprint arXiv:1703.04908 .
John F Nash Jr. 1950. The Bargaining Problem.
Econometrica: Journal of the Econometric Society
pages 155–162.
Yurii Nesterov. 1983. A Method of Solving a Convex
Programming Problem with Convergence Rate O
(1/k2). In Soviet Mathematics Doklady. volume 27,
pages 372–376.
Olivier Pietquin, Matthieu Geist, Senthilkumar Chandramohan, and Herve Frezza-Buet. 2011. Sample- ´
efficient Batch Reinforcement Learning for Dialogue Management Optimization. ACM Trans.
Speech Lang. Process. 7(3):7:1–7:21.
Verena Rieser and Oliver Lemon. 2011. Reinforcement
Learning for Adaptive Dialogue Systems: A Datadriven Methodology for Dialogue Management and
Natural Language Generation. Springer Science &
Business Media.
Alan Ritter, Colin Cherry, and William B Dolan. 2011.
Data-driven Response Generation in Social Media. In Proceedings of the Conference on Empirical
Methods in Natural Language Processing. Association for Computational Linguistics, pages 583–593.
Avi Rosenfeld, Inon Zuckerman, Erel Segal-Halevi,
Osnat Drein, and Sarit Kraus. 2014. NegoChat: A
Chat-based Negotiation Agent. In Proceedings of
the 2014 International Conference on Autonomous
Agents and Multi-agent Systems. International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC, AAMAS ’14, pages 525–532.
David Silver, Aja Huang, Chris J Maddison, Arthur
Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al. 2016. Mastering
the Game of Go with Deep Neural Networks and
Tree Search. Nature 529(7587):484–489.
Satinder Singh, Diane Litman, Michael Kearns, and
Marilyn Walker. 2002. Optimizing Dialogue Management with Reinforcement Learning: Experiments with the NJFun System. Journal of Artificial
Intelligence Research 16:105–133.
Victoria Talwar and Kang Lee. 2002. Development
of lying to conceal a transgression: Children’s control of expressive behaviour during verbal deception. International Journal of Behavioral Development 26(5):436–444.
David Traum, Stacy C. Marsella, Jonathan Gratch, Jina
Lee, and Arno Hartholt. 2008. Multi-party, Multiissue, Multi-strategy Negotiation for Multi-modal
Virtual Agents. In Proceedings of the 8th International Conference on Intelligent Virtual Agents.
Springer-Verlag, Berlin, Heidelberg, IVA ’08, pages
117–130.
Oriol Vinyals and Quoc Le. 2015. A Neural Conversational Model. arXiv preprint arXiv:1506.05869 .
Tsung-Hsien Wen, David Vandyke, Nikola Mrksic,
Milica Gasic, Lina M Rojas-Barahona, Pei-Hao Su,
Stefan Ultes, and Steve Young. 2016. A Networkbased End-to-End Trainable Task-oriented Dialogue
System. arXiv preprint arXiv:1604.04562 .
Jason D Williams and Steve Young. 2007. Partially
Observable Markov Decision Processes for Spoken
Dialog Systems. Computer Speech & Language
21(2):393–422.
Ronald J Williams. 1992. Simple Statistical Gradientfollowing Algorithms for Connectionist Reinforcement Learning. Machine learning 8(3-4):229–256.
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#D
Artificial Intelligence (AI) Chatbot as Language Learning
Artificial Intelligence (AI) Chatbot as Language Learning
#WakeUp
😈💩👎
Medium: An inquiry
Nuria Haristiani
Japanese Language Education Department, Universitas Pendidikan Indonesia, Jl. Dr.
Setiabudhi 229, Bandung, Indonesia
Abstract. In facing industry revolution 4.0, utilizing advanced information and computer
technology in educational environment is crucial. One of the advanced computation
technologies that can be used for learning, especially language learning, is chatbot. Chatbot is
a computer program based on artificial intelligence that can carry out conversations through
audio or text. This study intends to find out and analyze the types of artificial intelligence in
the form of chatbots and the possibility of their use as language learning medium. The data in
this study obtained from literature review on chatbot researches, and from observation results
on chatbot-based language learning medium developed by the author. The results indicated that
chatbots have a high potential to be used as a language learning medium, both as tutor in
practicing language, and as independent learning medium. Moreover, research results revealed
that language learners are interested in using chatbots because they can be used anytime and
anywhere, and they are more confident in learning languages using chatbots than when dealing
directly with human tutors.
1. Introduction
The industrial revolution 4.0 has an impact on the urgency of education field to be able to keep up
with these developments, which later brought the term Education 4.0 [1, 2, 3]. In embodying
Education 4.0, one of the most required ability from educators and educational practitioners is to be
able to integrate modern technology in their teaching [3]. The rapid development of smart phone
technology, social media, and artificial intelligence (AI), provide challenges for educational
practitioners to utilize these technologies in developing advance learning media. In latest decades,
artificial intelligence utilization to develop applications is massively conducted, and its products used
in almost every aspects of our life. This type of communication which occurs through digital
technology rather than in person is called computer-mediated communication (CMC) [4]. CMC forms
including instant messaging, email, chat rooms, online forums, social networks, and chatbot or
chatterbot [5, 6]. Chatbot is a computer program or artificial intelligence which carries out
conversations through audio or text [7], and interact with users in a particular domain or topic by
giving intelligent responses in natural language [8, 9]. Chatbot for general purposes and for
educational purposes have been developed [4,10,11]. However, despite chatbots’ unlimited possibility
to enhance language teaching and learning, the concept of chatbot including its advantages as
language learning medium is not yet widely known. Therefore, the purpose of this study is to analyze
the types of artificial intelligence in the form of chatbots and the possibility of their use as language
learning medium. This study also aims to observe a chatbot constructed as Japanese language learning
medium developed by the author and team, and reports its’ results as an inquiry to find out further
about the possibility of chatbot to enhance language learning and teaching.
International Conference on Education, Science and Technology 2019
Journal of Physics: Conference Series 1387 (2019) 012020
IOP Publishing
doi:10.1088/1742-6596/1387/1/012020
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2. Method
This study is a descriptive method study. The data collection in this study conducted through literature
reviews on previous researches about chatbot and its’ use. Literature reviews performed to identify the
type of chatbots, especially those developed for educational purposes including language learning, as
well as to identify their advantages/disadvantages in language teaching and learning. This study also
include a report on observation results on chatbot-based language learning medium developed by the
author and team, namely Gengobot.
3. Findings and Discussion
The basic mechanism of chatbot begins with the message sent by the user. The message then
processed by NLP (Natural Language Processing), and chatbot responded by replying to the message
according to the existing database (see figure 1). For example, when a user sent "how are you?"
message, chatbot will look for answers that match this question in the database such as “I am fine”,
“Great!” etc.
Figure 1.
The mechanism of
chatbot
.
3.1. The types of Chatbot
The type of chatbot found in this study can be categorized into three types based on its’ structure,
purpose, and audience. The sub-categories and their functions is concluded in table 1.
Table 1.
T
ypes of
chatbot
.
Category
Sub
-
category
Function
Structure Flow chatbot A tree-based chatbot. This chatbot has fixed responds set by the developer,
and only responds to questions that are already in the database. Flow chatbots
include buttons, keywords, and catchphrases instead of free writing to drive
the client down
the predefined path.
Artificially
intelligent
Chatbot with artificial intelligence has the ability update their knowledge and
perception from previous conversations and users’ experience, letting the
users engage more freely.
Hybrid This type of chatbot combines the concepts of Flow and AI chatbots. This
chatbot can understand and communicate with users, but remains in the
pattern determined by the developer.
Purpose Functionality This chatbots have certain functions depends on the developer (i.e. chatbot for
learning, personal assistant, reminder, online shop assistant, etc.)
Fun
Chatbot that intended only for entertainment (i.e. games,
funbot
, etc.).
Audience Generalist This chatbot has general knowledge that we can ask directly. I.e. Siri
developed by Apple ,and Cortana developed by Microsoft. Both Chatbots can
help us solve common problems such as searching for restaurants, locations
and more.
Specialist This chatbot focus on one constrained thing and do that one thing extremely
well
(i.e. chatbots that used t
o serve customers online when ordering items).
International Conference on Education, Science and Technology 2019
Journal of Physics: Conference Series 1387 (2019) 012020
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doi:10.1088/1742-6596/1387/1/012020
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Table 1 shows that chatbot has several categories and can be developed according to the developer’s
necessity. However, chatbots that are developed for educational purposes tend to use artificially
intelligence structure. Artificially intelligent chatbot for general purpose such as MILABOT has also
been developed [10]. MILABOT is a deep reinforcement learning chatbot which capable of
conversing with humans on popular small talk topics through both speech and text. The system
consists of an ensemble of natural language generation and retrieval models, including template-based
models, bag-of-words models, sequence-to-sequence neural network and latent variable neural
network models [10]. Chatbots that developed for educational purposes, especially for language
learning, are described further in the following sub-section.
3.2. Chatbot in Language Learning and Teaching
Chatbots development to utilize learning and teaching have been conducted. Freudbot has been
developed for psychology students to find out about student-content interaction in distance education.
The results shows that the basic analysis of the chatlogs indicated a high proportion of on-task
behavior. The findings also suggests that chatbot technology may be promising as a teaching and
learning tool in distance and online education [12]. Chatbot use is also compared with humanoid robot
in science lecture class, and reported that the visualization using chatbot was helpful for students to
understand the lecture smoothly [13]. However, researches on chatbot use and development to
enhance language learning rather difficult to find. This study identified researches on language
teaching and learning as seen in table 2.
Table 2.
Chatbot researches on language learning.
First author
(year)
Chatbot name Subject Focus Sample Research type
Jia (2004) - English,
Germany
as Foreign
Language
Application of a Web-
based Chatbot system
on foreign language
teaching
1256 Experiment
Fryer (2006) Cleverbot English as
Foreign
Language
Chatbot as English
language learning tools
211 Experiment &
survey
Jia (2009) CSIEC chatbot English
Learning
A computer assisted
English learning chatbot
based on textual
knowledge and
reasoning
1783
Experiment, survey
& questionnaire
Goda (2014) Cleverbot English as
Foreign
Language
The use of Chatbot
before online EFL
discussion and The
effect on critical
thinking
130 Comparison based
(Experimental &
Control group)
Fryer (2017) Cleverbot English as
Foreign
Language
Comparison of chatbot
and human task partners
in English learning
122 Comparison based
(Pre-test & Post-
test)
Table 2 shows that chatbot researches were mainly found in English language learning
[14,15,16,17,18]. Research reported that the dialogs using chatbot are mostly very short because the
users find the computer is much less intelligent as a human, since the responses from the computer are
often repeated and irrelevant with the topics and the context. However, the results also indicates that
International Conference on Education, Science and Technology 2019
Journal of Physics: Conference Series 1387 (2019) 012020
IOP Publishing
doi:10.1088/1742-6596/1387/1/012020
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many participants are very interested in using chatbot as chatting partner in speaking foreign language,
since it is accessible anywhere and anytime, while it is not easy to find native speakers as human
chatting partner. The learners also more confident communicating with chatbot which is obviously
less intelligent as the human themselves. It would be pedagogically attractive for the learner to chat
with a system of artificial intelligence which could “really” understand the natural language and
communicatively generate the natural language to form a human-like dialog [14].
Further, the main chatbot used in language learning researches as seen in Table 2 is Cleverbot, which
developed by British AI scientist Rollo Carpenter in 1986 and went online on 1997. The results of
researches of using Cleverbot reported that most students enjoyed using this chatbot [15,17]. They
also generally felt more comfortable conversing with the bots than a student partner or teacher.
However, the results also suggest that chatbots are generally only useful for advanced and/or very
keen language students. Language teachers also need to get involved and bring chatbot technology
into the foreign language learning classroom as a permanent tool for language practice [15]. Research
reported that preceding conversation before classroom discussion with a chatbot lead to an increase in
the number of contributions that students made to discussion [17]. Moreover, pre-discussion with a
chatbot also could increase the students’ awareness of critical thinking and enable them to form
inquiring mindsets [17]. However, the result of comparisons in speaking task with chatbot and human
partner indicated a significant drop in students' task interest with chatbot, but not human partner. The
reason of drop in task interest with chatbot was caused by novelty effect [18]. On the other hand,
CSIEC chatbot reported successfully helped students with course unit review, make the students more
confident, and improved students’ listening ability, as well as enhanced students’ interest in language
learning. The comparison of examination results before and after the using chatbot showed great
improvement of students’ performance [15].
From above results, it is understood that the use of chatbot gave many advantages in language learning
and teaching, as in enhancing classroom motivation and learning [15,19]. However, chatbot also
reported to have flaws comparing to human partner, especially in the novelty aspect [18]. Several
researches and chatbot development for English language learning have been conducted, but chatbot
development and researches in other languages teaching and learning is still difficult to find. As an
attempt to answer this challenge, the author and team tried to develop a chatbot-based multi-language
grammar application, namely Gengobot, which will be introduced further in the next sub-section.
3.3. Gengobot as Japanese Language Learning Medium
Gengobot is a chatbot-based dictionary application about multi-language grammar developed by the
author, using CodeIgniter (CI) framework. CI is a PHP framework that can be used to develop PHP-
based website application without the necessity to write all the code from the beginning. CI framework
was chosen because it is an opensource framework and free to modify, smaller than other framework,
and uses MVC (Model-View-Controller) concept that functioned in programming process to call
needed databases easily. The main purpose of Gengobot development is to provide a Japanese
grammar learning medium for beginner level of Japanese language learners. However, to broaden its’
use, the application also equipped with grammar contents in English and Indonesian, and integrated
with social media LINE. LINE official account has Messaging API feature that allows an account to
run chatbot that has been created (see figure 2). The database system used in Gengobot is MySQL (see
figure 3 and figure 4). MySQL was chosen because it is free licensed, and the database structure used
in MySQL is in table form which is flexible and easy to use. The database created for this chatbot
including: (1) Database of user data storage, including name, language, training score, etc.; (2)
Grammar database in three languages; (2) Questions database and their answer (for ‘Exercise’
feature).
International Conference on Education, Science and Technology 2019
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Figure 2. LINE Messaging API and chatbot
mechanism.
Figure 3. Gengobot database creation using
MySQL
.
Figure 4. The process of importing database using
MySQL.
Figure 5. Gengobot interface designing process.
Gengobot consists of fixed responds including buttons, keywords, and catchphrases set as database.
The buttons includes several menus as ‘Menu’, ‘Help’, ‘Language’, and ‘Contact’ option as shown in
figure 5, as well as several sub-menus that developed under each menu. From observation and
evaluation results, Gengobot was successfully developed as a language learning medium, especially to
support students’ learning about Japanese grammar with description available in English and
Indonesian. Gengobot also considered user friendly since it could be accessed through social media
LINE. The features in Gengobot also includes ‘Exercise’, so the students not only able to learn about
Japanese grammars, but also able to test their knowledge about grammars they have learned. However,
Gengobot is a flow chatbot, so the interaction between users/students and chatbot still very limited to
the inputted database. Although as grammar learning application it is still considered sufficient, to
improve its’ use for enhancing language learning, as well as other language skills teaching and
learning, Gengobot still need to be developed further in its’ technology and features.
4. Conclusion
This study aimed to analyze the types of chatbots and the possibility of their use as language learning
medium. From the results, it is known that chatbot can be categorized into three types, and has
advantages and disadvantages. As the advantages, chatbot is reported can help language learners
through six ways: (1) students tend to feel more relaxed talking to a computer than to a person; (2)
chatbots are willing to repeat the same material with students endlessly; (3) many bots provide both
text and synthesized speech, allowing students to practice both listening and reading skills; (4) Bots
are new and interesting to students; (5) students have an opportunity to use a variety of language
structures and vocabulary that they ordinarily would not have a chance to use; (6) chatbots could
potentially provide quick and effective feedback for students’ spelling and grammar [15]. However,
chatbot also reported to have a flaw on its novelty aspects and need to be improved. This study also
observed a chatbot-based Japanese language learning medium developed by the author, namely
Gengobot. As the results, Gengobot have a high potential to be used as a Japanese language learning
medium especially in learning grammar, yet need to be developed further in its’ technology and
features.
International Conference on Education, Science and Technology 2019
Journal of Physics: Conference Series 1387 (2019) 012020
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doi:10.1088/1742-6596/1387/1/012020
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5. References
[1] Harkins A M 2008 Leapfrog principles and practices: Core components of education 3.0 and 4.0.
Futures Research Quarterly 24(1) pp 19-31
[2] Puncreobutr V 2016 Education 4.0: new challenge of learning St. Theresa Journal of
Humanities and Social Sciences 2(2)
[3] Hussin A A 2018 Education 4.0 Made Simple: Ideas For Teaching International Journal of
Education and Literacy Studies 6(3) pp 92-8
[4] Hill J Ford W R and Farreras I G 2015 Real conversations with artificial intelligence: A
comparison between human–human online conversations and human–chatbot conversations.
Computers in Human Behavior 49 pp 245-50
[5] Tagliamonte S A and Denis D 2008 Linguistic ruin? LOL! Instant messaging and teen language
American speech 83(1) pp 3-4
[6] Thurlow C L Lengel L L and Tomic A 2004 Computer Mediated Communication: Social
Interaction and the Internet
[7] Shevat A 2017 Designing bots: creating conversational experiences O'Reilly Media, Inc
[8] Abdul-Kader S A and Woods J C 2015 Survey on chatbot design techniques in speech
conversation systems International Journal of Advanced Computer Science and Applications
6(7)
[9] Azwary F Indriani F and Nugrahadi D T 2016 Question Answering System Berbasis Artificial
Intelligence Markup Language Sebagai Media Informasi KLIK-KUMPULAN JURNAL
ILMU KOMPUTER 3(1) pp 48-60
[10] Serban I V Sankar C Germain M Zhang S Lin Z Subramanian S Kim T Pieper M Chandar S Ke
N R and Rajeshwar S 2017 A deep reinforcement learning chatbot. arXiv preprint
arXiv:1709.02349
[11] Chen J A Tutwiler M S Metcalf S J Kamarainen A Grotzer T and Dede C 2016 A multi-user
virtual environment to support students' self-efficacy and interest in science: A latent growth
model analysis Learning and Instruction 41 pp 11-22
[12] Heller B Proctor M Mah D Jewell L and Cheung B 2005 Freudbot: An investigation of chatbot
technology in distance education InEdMedia+ Innovate Learning pp 3913-3918
[13] Matsuura S and Ishimura R 2017 Chatbot and dialogue demonstration with a humanoid robot in
the lecture class. International Conference on Universal Access in Human-Computer
Interaction pp 233-246
[14] Jia J 2004 The study of the application of a web-based chatbot system on the teaching of foreign
languages. Society for Information Technology & Teacher Education International
Conference pp 1201-1207
[15] Fryer L and Carpenter R 2006 Bots as language learning tools Language Learning &
Technology 10(3) pp 8-14
[16] Jia J 2009 CSIEC: A computer assisted English learning chatbot based on textual knowledge
and reasoning Knowledge-Based Systems 22(4) pp 249-55
[17] Goda Y Yamada M Matsukawa H Hata K and Yasunami S 2014 Conversation with a chatbot
before an online EFL group discussion and the effects on critical thinking. The Journal of
Information and Systems in Education 13(1) pp 1-7
[18] Fryer L K Ainley M Thompson A Gibson A and Sherlock Z 2017 Stimulating and sustaining
interest in a language course: An experimental comparison of Chatbot and Human task
partners Computers in Human Behavior 75 pp 461-8
[19] Coniam D 2008 Evaluating the language resources of chatbots for their potential in English as a
second language ReCALL 20(1) pp 98-116
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