A Polyphonic Model and System forInter-animation Analysis in Chat Conversations with Multiple Participants

Ştefan Trăuşan-Matu, Traian RebedeaUniversitatea “Politehnica” Bucureşti,

Institutul de Cercetări în Inteligenţă Artificială al Academiei Române

Outlook� Chat Conversations with Multiple Participants

� A Polyphonic Model of Discourse

� Inter-animation Analysis

� The PolyCAFe Analysis System

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Chat Conversations with

Multiple Participants� Multiple participants (≥3), conferencing style

� Very important in the context of the spread of forums and instant messengers – chats

� Important tool for� Important tool for� Computer-Supported Collaborative Learning (CSCL)

� Cooperative work online

� Particular features – multiple, parallel discussion chains !!!

� There is a need for� Determining important utterances

� Contributions of the participants

� Degree of collaboration - inter-animation analysis


Example: CSCL assignment� Students had to debate in chat sessions in groups

ranging from 3 to 8

� In the first part of the conversation, each student had to defend a technology by presenting its features and to defend a technology by presenting its features and advantages and criticize the others by invoking their flaws and drawbacks

� In the final part of the chat, they had to discuss on how they could integrate all these technologies in a single online collaboration platform


CSCL assignment: Problems�How to assist teachers in evaluating students’ work in chats?

�Offer assistance to students

�Abstraction tools�Abstraction tools

�Automatic feedback

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Experiments with

chat-based CSCL

�K-12 students solving mathematics problems both individually and collaboratively in the VMT project at Drexel University, Philadelphia, USA

Computer Science students at Bucharest �Computer Science students at Bucharest “Politehnica” University, Romania at

�Human-Computer Interaction course in Romanian and French – role playing and debate�Natural Language Processing - role playing and debate�Algorithm Design – problem solving


The VMT chat environment

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VMT Referencing facility


Discourse� Monologue

� Unidirectional model of communication, from a speaker to a listener – theories and methods of analysis:� Rhetorical Schema Theory

� Centering TheoryCentering Theory

� Co-reference resolution

� Dialogue� Phone-like, face to face style – units of analysis:

� Speech acts

� Dialog acts

� Adjacency pairs

� Multi-parties conversation


Theories for analysing

multi-parties conversation

� Discourse analysis (Tannen)

� Conversation analysis (Sacks, Jefferson, Schegloff)

� Accountable talk (Resnick)� Accountable talk (Resnick)

� Transactivity (Teasley, Berkowitz & Gibbs, Joshi & Rose)

� Inter-animation (Bakhtin, Wegerif, Trausan-Matu)

� Polyphony (Bakhtin, Trausan-Matu et al.)

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Transactivity analysis� TF-IDF

� Latent Semantic Analysis

� Naïve Bayes

Social Network Analysis� Social Network Analysis

� WordNet (wordnet.princeton.edu)

� Support Vector Machines

� Collin’s perceptron

� TagHelper environment


Transactivity analyis� TF-IDF

� Latent Semantic Analysis Almost all are based also on

� Naïve Bayes a two interlocutors

Social Network Analysis model, in which� Social Network Analysis model, in which

� WordNet (wordnet.princeton.edu) one person speaks

� Support Vector Machines at a time, resulting

� Collin’s perceptron one discussion thread

� TagHelper environment


A socio-cultural perspective on Natural

Language

�Sfard: “rather than speaking about ‘acquisition of knowledge,’ many people prefer to view learning as becoming a participant in a certain discourse” (2000)

�Wertch: Lotman - text is a „thinking device” (1981)�Wertch: Lotman - text is a „thinking device” (1981)

�Stahl “to learn is to become a skilled member of communities of practice …. and to become competent at using their …. speech genres” (2006)

�Koshmann: “the voices of others become woven into what we say, write, and think” (1999)

�Wegerif - teaching thinking skills by inter-animation: “meaning-making requires the inter-animation of more than one perspective“ (2005)


Dialogism – Mikhail Bakhtin

• Basis for the CSCL paradigm (Koschman, 1999)• “… Any true understanding is dialogic in nature”

(Voloshinov-Bakhtin, 1973)• Opposed to de Saussure ideas:

Real life dialog should be the focus, not written text• Real life dialog should be the focus, not written text• Words are not arbitrary

• Utterances (not sentences) should be the unit of analysis

• Speech genres� Polyphony � Inter-animation of voices


Polyphony and counterpoint� Concept derived from classical music

� “These are different voices singing variously on a single theme. This is indeed 'multivoicedness,' exposing the diversity of life and the great complexity of human experience. 'Everything in life is counterpoint, that is, opposition,' “ (Bakhtin, 1984)of human experience. 'Everything in life is counterpoint, that is, opposition,' “ (Bakhtin, 1984)

� Multiple voices – each utterance contains multiple voices

� Voices inter-animate in an unmerged way: � “a plurality of independent and unmerged voices and

consciousnesses” (Bakhtin)


Polyphonic inter-animation


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Polyphony


Words, voices and threads� Positions assigned to participants – voices� Additional voices – frequent concepts – repeated

words become voices, stronger or weaker� Voices continue and influence each other through � Voices continue and influence each other through

explicit or implicit links. � Voices correspond to chains or threads of utterances:

� repeated words� lexical chains� co-references� reasoning or argumentation� rhetorical schemas


LTfLL - EU FP7 Project

(2008-2011)

� Language Technologies for Lifelong Learning

� Netherlands, France, United Kingdom, Germany, Ausria, Romania, BulgariaRomania, Bulgaria

� PolyCAFe system (Polyphony-based Collaboration Analysis and Feedback generation)

� The system has just been validated with students and tutors




NLP pipe� spelling correction, stemmer, tokenizer, Named Entity

Recognizer, POS tagger and parser, and NP-chunker. Stanford NLP software (http://nlp.stanford.edu/software)

� Spellchecker : Jazzy � Spellchecker : Jazzy http://www.ibm.com/developerworks/java/library/j-jazzy/

� Alternative NLP pipes are under development,

� GATE (http://gate.ac.uk)

� LingPipe (http://aliasi.com/lingpipe/).


Pattern Language

� Regular expression search

� Synonyms, hypernyms and hyponyms via WordNet

� Words’ stems and their part of speech (POS)� Words’ stems and their part of speech (POS)

� consideration of utterances as a search unit, for example, specifying that a word should be searched in the previous n utterances and that two expressions should be in two utterances


Pattern Language (ex.)

� <S "convergence"> #[*] cube searches pairs of utterances that have a synonym of “convergence” in the first utterance and “cube” in the secondfirst utterance and “cube” in the second

� 1103 # 1107. overlap # cube [that would stil have to acount for the overlap that way] # [an idea: Each cube is assigned to 3 edges. Then add the edges on the diagonalish face.]


Classical NLP analysis

(content and discourse analysis)� Identification of concepts (using NLP pipe, pattern

language, cue-phrases and graph algorithms)

� Utterance features:� Speech acts� Speech acts

� Argumentation types in utterances (as in Toulmin’s theory Warrant, Concession, Rebuttal and Qualifiers)

� Implicit links:� Repetitions

� Adjacency pairs

� Co-references (with the BART system http://bart-coref.org/)


Social network analysis� Consider explicit and implicit referencing as arcs

between participants, which are the nodes

� A kind of page-rank algorithm – an utterance is important if it is referred by important utterances; The important if it is referred by important utterances; The strength of a voice (of an utterance) depends on the strength of the utterances that refer to it

� Determines if a person is central/peripheral


Polyphony, Inter-animation and

Collaboration analysis� Assign an importance value for each utterance

considering several indicators of inter-animation (collaboration)� Detection of voices (chains) inter-animation patterns � Detection of voices (chains) inter-animation patterns

(Trausan-Matu) in the chat� Consider several criteria such as the presence in the chat

of questions, agreement, disagreement� Presence of others’ voices� Social Networks metrics

� Machine learning approach (genetic algorithms and neural networks) for tuning the







Thank You!

Questions?Questions?

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March 23, 2010

Technology

A Polyphonic Model and System forInter-animation Analysis in Chat Conversations with Multiple Participants