Classifying Argumentative Relations Using Logical Mechanisms and Argumentation Schemes

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.

Characteristics of persuasive deltaboard members on Reddit’s r/ChangeMyView

“…Specifically, we measured the density of words from each of the 10 moral foundations in the corpus of comments, using the Moral Foundations Dictionaries for Linguistic Analyses, 2.0 (Frimer, Boghrati, Haidt, Graham, & Dehgani, 2019). Some work suggests more effective arguments attack sentences which are causally central to arguments on ChangeMyView (Jo, Bang, Reed, & Hovy, 2021). Thus, we also sought to determine the effect of causal reasoning, as previous findings suggest that arguments about causation are amongst the most likely to result in attitude change (Na & DeDeo, n.d.).…”

Uncovering Implicit Inferences for Improved Relational Argument Mining

“…Various approaches have been proposed to infer argumentative structure from unstructured text. These include Long-Short Term Memory (LSTM) models (Cocarascu and Toni, 2017;Paul et al, 2020), pre-trained transformers (Ruiz-Dolz et al, 2021) and logic programming (Jo et al, 2021). Pretrained transformers, in particular, have been shown to perform exceptionally well on this task with no additional feature engineering (Ruiz-Dolz et al, 2021;Fromm et al, 2019); this suggests that the introduction of external knowledge encoded within the transformers due to pre-training on large corpora is necessary to make significant progress on this task.…”

Have my arguments been replied to? Argument Pair Extraction as Machine Reading Comprehension

“…Argument mining aims to analyze the structure of argumentation, and it contains various subtasks, such as argument component identification (Moens et al, 2007;Goudas et al, 2015;Ajjour et al, 2017;Jo et al, 2019), argument relation prediction (Nguyen and Litman, 2016;Cocarascu et al, 2020;Jo et al, 2021), argumentation structure parsing (Stab and Gurevych, 2017;Kuribayashi et al, 2019;Morio et al, 2020;Bao et al, 2021), argumentation strategy analysis (Khatib et al, 2018;Morio et al, 2019), etc.…”