An Empirical Study of Span Representations in Argumentation Structure Parsing

Abstract

Argumentation Structure Parsing (ASP) is the task of predicting the roles of argumentative units (e.g., claim, premise) and the relations between the units (e.g., support, attack) in an argumentative text. ASP has received a great deal of attention due to its usefulness for applications such as automatic assessment of argumentative texts. As textual spans (i.e., argumentative units) are basic units of ASP, it is important to explore an effective design for representing them. Inspired by the current span representation design in other natural language processing tasks, we propose a method to obtain effective span representations of argumentative units in ASP. Our proposed method leverages multiple levels of global contextual information, such as argumentative markers in surrounding contexts, for obtaining each span representation. We show that using our span representation improves performance on several benchmark datasets—especially when parsing complex argumentative texts, which have been difficult to parse with existing methods. Furthermore, we report the effectiveness of our span representations when using word representations obtained from existing, powerful language models such as BERT.