Research Institute: IRIT, CNRS \& Université Paul Sabatier,
Supervisors: Leila Amgoud, Nicholas Asher, Stergos Afantenos
TITLE: Argumentation in Dialogue
In a persuasion dialogue, participants often have conflicting
opinions and everyone tries to convince others. This involves an
exchange of arguments which may be in conflict or not. Such a
dialogue ends with either a failure, where the disagreement
persists, or agreement.
Persuasion dialogues interest linguists as well as researchers in
Artificial Intelligence (AI). The first develop theories of
discourse and dialogue. These theories allow fine linguistic
analysis of texts, and in particular analysis of the arguments
present therein. The linguistic foundations enable empirical
analysis of the arguments where the goal is the automatic
extraction of the relational structure that underpins those
arguments. As far as AI is concerned, the focus is on developing
models of argumentation and criteria for evaluating them.
Arguments are often seen as abstract entities whose origin and
nature are undefined.
The PhD thesis we propose puts both expertise together in order
to build models of argumentation and in accordance with rich
dialogues in natural language. The thesis will first examine the
state of the art in both areas, and build bridges between them.
Discourse theories will give the argumentation theory rich data
with a semantic and logical analysis. These data concern both the
arguments and the relationships that exist between them. This in
turn helps define appropriate criteria for evaluating arguments.
These criteria are validated in the texts. The osmosis might also
happen the other way around, where argumentation theory might
enhance and inform discourse analysis.
The final theory that will result will have to be empirically
validated using Machine Learning approaches. The problem of
identifying the underlying argumentation structure is an
extremely attractive one, since it involves not simply the
prediction of isolated arguments or relations but instead the
prediction of the whole argumentation graph. By consequence
methods from structured prediction will need to be exploited.
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