Stance Prediction for Contemporary Issues: Data and Experiments

TL;DR

This paper introduces a new stance detection dataset for contemporary issues and demonstrates that simple neural models with sentiment or emotion cues can perform competitively against complex transformers, with added interpretability.

Contribution

It presents a novel large-scale stance detection dataset and compares simple neural models with transformers, highlighting efficiency and interpretability advantages.

Findings

Simple RNNs with sentiment/emotion info are competitive with BERT.

A straightforward method explains input phrase contributions.

The dataset covers 419 controversial issues.

Abstract

We investigate whether pre-trained bidirectional transformers with sentiment and emotion information improve stance detection in long discussions of contemporary issues. As a part of this work, we create a novel stance detection dataset covering 419 different controversial issues and their related pros and cons collected by procon.org in nonpartisan format. Experimental results show that a shallow recurrent neural network with sentiment or emotion information can reach competitive results compared to fine-tuned BERT with 20x fewer parameters. We also use a simple approach that explains which input phrases contribute to stance detection.