Journal
TRANSACTIONS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS
Volume 9, Issue -, Pages 721-739Publisher
MIT PRESS
DOI: 10.1162/tacl_a_00394
Keywords
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Funding
- Kwanjeong Educational Foundation
- Volkswagen Stiftung [92 182]
- ESRC [ES/V003901/1]
- EPSRC [EP/N014871/1]
- CMU's GuSH Research grant
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While current research on argument mining lacks a comprehensive understanding of the logical mechanisms that constitute argumentative relations, this study attempts to classify argumentative relations based on four logical and theory-informed mechanisms, achieving better results than unsupervised baselines.
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.
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