Topic-independent Detection of Dissonance in Short Stance Text

Anonymous

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.