Rhetoric, Logic, and Dialectic: Advancing Theory-based Argument Quality Assessment in Natural Language Processing

Preceding work in natural language processing (NLP) and computational linguistics (CL) has mostly focused on practical AQ assessment, considering either the overall quality of arguments (Toledo et al., 2019; Gretz et al., 2020, inter alia) or a single specific conceptualization of AQ, e.g., argument strength (Persing and Ng, 2015), convincingness (Habernal and Gurevych, 2016), and relevance (Wachsmuth et al., 2017c). However, Gretz et al. (2020) note the need to predict quality in terms of fine-grained aspects. Fine-grained prediction enables a deeper understanding of argumentation and offers specific feedback to authors aiming to improve their argumentative writing skills. For instance, authors might want to know whether their premises are sufficient with regard to their claim(s) or whether their language is appropriate. Wachsmuth et al. (2017b) surveyed and synthesized theory-based dimensions of AQ into a taxonomy of three main dimensions: Cogency (Logic), Effectiveness (Rhetoric), and Reasonableness (Dialectic).