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

Ştefan Trăuşan-Matu, Traian Rebedea
Universitatea “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

March 23, 2010 Cicling 2010 - Iasi, Romania 2

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
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
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
Automatic feedback

March 23, 2010 Cicling 2010 - Iasi, Romania March 23, 2010 5

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
“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

Cicling 2010 - Iasi, Romania March 23, 2010 7

VMT Referencing facility


Discourse
Monologue
Unidirectional model of communication, from a speaker to a
listener – theories and methods of analysis:
Rhetorical Schema Theory
Centering 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)
Transactivity (Teasley, Berkowitz & Gibbs, Joshi &
Rose)
Inter-animation (Bakhtin, Wegerif, Trausan-Matu)
Polyphony (Bakhtin, Trausan-Matu et al.)


Transactivity analysis
TF-IDF
Latent Semantic Analysis
Naïve Bayes
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
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)
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
• 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)
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
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, Bulgaria

PolyCAFe system (Polyphony-based Collaboration Analysis
and Feedback generation)
The system has just been validated with students and tutors


March 23, 2010 Cicling 2010 - Iasi, Romania March 23, 201021

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
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)
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 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
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
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
(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?

34
March 23, 2010 Cicling 2010 - Iasi, Romania

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

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