Improving Multi-task Stance Detection with Multi-task Interaction Network

Abstract

Stance detection aims to identify people's standpoints expressed in the text towards a target, which can provide powerful information for various downstream tasks. Recent studies have proposed multi-task learning models that introduce sentiment information to boost stance detection. However, they neglect to explore capturing the fine-grained task-specific interaction between stance detection and sentiment tasks, thus degrading performance. To address this issue, this paper proposes a novel multi-task interaction network (MTIN) for improving the performance of stance detection and sentiment analysis tasks simultaneously. Specifically, we construct heterogeneous taskrelated graphs to automatically identify and adapt the roles that a word plays with respect to a specific task. Also, a multi-task interaction module is designed to capture the wordlevel interaction between tasks, so as to obtain richer task representations. Extensive experiments on two real-world datasets show that our proposed approach outperforms state-ofthe-art methods in both stance detection and sentiment analysis tasks.