Exploring Discourse Structures for Argument Impact Classification (2021)

Abstract: Discourse relations among arguments reveal logical structures of a debate conversation. However, no prior work has explicitly studied how the sequence of discourse relations influence a claim’s impact. This paper empirically shows that the discourse relations between two arguments along the context path are essential factors for identifying the persuasive power of an argument. We further propose DisCOC to inject and fuse the sentence-level structural discourse information with contextualized features derived from large-scale language models. Experimental results and extensive analysis show that the attention and gate mechanisms that explicitly model contexts and texts can indeed help the argument impact classification task defined by Durmus et al. (2019), and discourse structures among the context path of the claim to be classified can further boost the performance.

## introduction it is an interesting natural language understanding problem to identify the impact and the persuasiveness of an argument in a conversation. previous works have shown that many factors can affect the persuasiveness prediction, ranging from textual and argumentation features @xcite , style factors @xcite , to the traits of source or audience @xcite @xcite . discourse relations, such as restatement and instantiation, among arguments reveal logical structures of a debate conversation. it is natural to consider using the discourse structure to study the argument impact. @xcite initiated a new study of the influence of discourse contexts on determining argument quality by constructing a new dataset kialo. ## related work there is an increasing interest in computational argumentation to evaluate the qualitative impact of arguments based on corpus extracted from web argumentation sources such as cmv sub-forum of reddit @xcite . studies explored the importance and effectiveness of various factors on determining the persuasiveness and convincingness of arguments, such as surface texture, social interaction and argumentation related features @xcite , characteristics of the source and audience @xcite @xcite , sequence ordering of arguments @xcite , and argument structure features @xcite . the style feature is also proved to be significant in evaluating the persuasiveness of news editorial argumentation @xcite . @xcite conducted experiments in an entirely empirical manner, constructing a corpus for argument quality label classification and proposing several neural network models. in addition to the features mentioned above, the role of pragmatic and discourse contexts has shown to be crucial by not yet fully explored. @xcite examined how the contexts and the dynamic progress of argumentative conversations influence the comparative persuasiveness of an argumentation process. @xcite created a new dataset based on argument claims and impact votes from a debate platform kialo.com, and experiments showed that incorporating contexts is useful to classify the argument impact. understanding discourse relations is one of the fundamental tasks of natural language understanding, and it is beneficial for various downstream tasks such as sentiment analysis @xcite , machine translation @xcite ) and text generation @xcite . discourse information is also considered indicative for various tasks of computational argumentation. @xcite analyzed the role of discourse markers for discriminating claims and premises in argumentative discourse and found that particular semantic group of discourse markers are highly predictive features. @xcite concatenated sentence vectors with discourse relation embeddings as sentence features for persuasiveness prediction and showed that discourse embeddings helped improve performance. ## conclusion in this paper, we explicitly investigate how discourse structures influence the impact and the persuasiveness of an argument claim. we present discoc to produce discourse-dependent contextualized representations. experiments and ablation studies show that our model improves its backbone roberta around 1.67%. instead, han and other attention mechanisms bring side effects. we discover distinct discourse relation path patterns and analyze representatives. in the future, we plan to explore discourse structures in other nlu tasks.