%0 Conference Proceedings %T Improving Multi-task Stance Detection with Multi-task Interaction Network %A Chai, Heyan %A Tang, Siyu %A Cui, Jinhao %A Ding, Ye %A Fang, Binxing %A Liao, Qing %Y Goldberg, Yoav %Y Kozareva, Zornitsa %Y Zhang, Yue %S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing %D 2022 %8 December %I Association for Computational Linguistics %C Abu Dhabi, United Arab Emirates %F chai-etal-2022-improving %X 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 task-related 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 word-level interaction between tasks, so as to obtain richer task representations. Extensive experiments on two real-world datasets show that our proposed approach outperforms state-of-the-art methods in both stance detection and sentiment analysis tasks. %R 10.18653/v1/2022.emnlp-main.193 %U https://aclanthology.org/2022.emnlp-main.193 %U https://doi.org/10.18653/v1/2022.emnlp-main.193 %P 2990-3000