Classifying Argumentative Relations Using Logical Mechanisms and Argumentation Schemes

Yohan Jo, Seojin Bang, Chris Reed, Eduard Hovy


Abstract
While argument mining has achieved significant success in classifying argumentative relations between statements (support, attack, and neutral), we have a limited computational understanding of logical mechanisms that constitute those relations. Most recent studies rely on black-box models, which are not as linguistically insightful as desired. On the other hand, earlier studies use rather simple lexical features, missing logical relations between statements. To overcome these limitations, our work classifies argumentative relations based on four logical and theory-informed mechanisms between two statements, namely, (i) factual consistency, (ii) sentiment coherence, (iii) causal relation, and (iv) normative relation. We demonstrate that our operationalization of these logical mechanisms classifies argumentative relations without directly training on data labeled with the relations, significantly better than several unsupervised baselines. We further demonstrate that these mechanisms also improve supervised classifiers through representation learning.
Anthology ID:
2021.tacl-1.44
Volume:
Transactions of the Association for Computational Linguistics, Volume 9
Month:
Year:
2021
Address:
Cambridge, MA
Venue:
TACL
SIG:
Publisher:
MIT Press
Note:
Pages:
721–739
Language:
URL:
https://aclanthology.org/2021.tacl-1.44
DOI:
10.1162/tacl_a_00394
Bibkey:
Cite (ACL):
Yohan Jo, Seojin Bang, Chris Reed, and Eduard Hovy. 2021. Classifying Argumentative Relations Using Logical Mechanisms and Argumentation Schemes. Transactions of the Association for Computational Linguistics, 9:721–739.
Cite (Informal):
Classifying Argumentative Relations Using Logical Mechanisms and Argumentation Schemes (Jo et al., TACL 2021)
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PDF:
https://aclanthology.org/2021.tacl-1.44.pdf