13,005 research outputs found
Online Handbook of Argumentation for AI: Volume 1
This volume contains revised versions of the papers selected for the first
volume of the Online Handbook of Argumentation for AI (OHAAI). Previously,
formal theories of argument and argument interaction have been proposed and
studied, and this has led to the more recent study of computational models of
argument. Argumentation, as a field within artificial intelligence (AI), is
highly relevant for researchers interested in symbolic representations of
knowledge and defeasible reasoning. The purpose of this handbook is to provide
an open access and curated anthology for the argumentation research community.
OHAAI is designed to serve as a research hub to keep track of the latest and
upcoming PhD-driven research on the theory and application of argumentation in
all areas related to AI.Comment: editor: Federico Castagna and Francesca Mosca and Jack Mumford and
Stefan Sarkadi and Andreas Xydi
How did the discussion go: Discourse act classification in social media conversations
We propose a novel attention based hierarchical LSTM model to classify
discourse act sequences in social media conversations, aimed at mining data
from online discussion using textual meanings beyond sentence level. The very
uniqueness of the task is the complete categorization of possible pragmatic
roles in informal textual discussions, contrary to extraction of
question-answers, stance detection or sarcasm identification which are very
much role specific tasks. Early attempt was made on a Reddit discussion
dataset. We train our model on the same data, and present test results on two
different datasets, one from Reddit and one from Facebook. Our proposed model
outperformed the previous one in terms of domain independence; without using
platform-dependent structural features, our hierarchical LSTM with word
relevance attention mechanism achieved F1-scores of 71\% and 66\% respectively
to predict discourse roles of comments in Reddit and Facebook discussions.
Efficiency of recurrent and convolutional architectures in order to learn
discursive representation on the same task has been presented and analyzed,
with different word and comment embedding schemes. Our attention mechanism
enables us to inquire into relevance ordering of text segments according to
their roles in discourse. We present a human annotator experiment to unveil
important observations about modeling and data annotation. Equipped with our
text-based discourse identification model, we inquire into how heterogeneous
non-textual features like location, time, leaning of information etc. play
their roles in charaterizing online discussions on Facebook
Argumentation Theory for Mathematical Argument
To adequately model mathematical arguments the analyst must be able to
represent the mathematical objects under discussion and the relationships
between them, as well as inferences drawn about these objects and relationships
as the discourse unfolds. We introduce a framework with these properties, which
has been used to analyse mathematical dialogues and expository texts. The
framework can recover salient elements of discourse at, and within, the
sentence level, as well as the way mathematical content connects to form larger
argumentative structures. We show how the framework might be used to support
computational reasoning, and argue that it provides a more natural way to
examine the process of proving theorems than do Lamport's structured proofs.Comment: 44 pages; to appear in Argumentatio
A Discussion Game for the Credulous Decision Problem of Abstract Dialectical Frameworks under Preferred Semantics
Abstract dialectical frameworks (ADFs) have been introduced as a general formalism for modeling and evaluating argumentation. However, the role of discussion in reasoning in ADFs has not been clarified well so far. The current work presents a discussion game, as a proof method, to answer credulous decision problems of ADFs under preferred semantics. The game can be the basis for an algorithm that can be used not only for answering the decision problem but also for human-machine interaction
A Model for Processing Illocutionary Structures and Argumentation in Debates
International audienc
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