  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.
