Topic-independent Detection of Dissonance in Short Stance Text

Abstract

We address dissonance detection, the task of detecting conflicting stance between two input statements. Computational models for stance detection have typically been trained for a given target topic (e.g. gun control). In this paper, we aim for building a computational model for dissonance detection without using training data from the topic of test data. We first build a large-scale dataset of topic-controlled arguments from two sources: (i) an online debate platform, consisting of 15k pairs of statements with support, attack, or no relation from 20 diverse topics, and (ii) Twitter, consisting of 5k pairs of statements from 5 topics. We then evaluate a BERT-based dissonance detection model on this dataset in a topic-controlled manner. Our experiments suggest that dissonance detection models learn the topic-independent patterns of language for detecting dissonance and generalize largely to other arguments in unseen topics.

Tasks

Reproductions