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    <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 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.</abstract>
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%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

Markdown (Informal)

[Improving Multi-task Stance Detection with Multi-task Interaction Network](https://aclanthology.org/2022.emnlp-main.193) (Chai et al., EMNLP 2022)

• Improving Multi-task Stance Detection with Multi-task Interaction Network (Chai et al., EMNLP 2022)

ACL

• Heyan Chai, Siyu Tang, Jinhao Cui, Ye Ding, Binxing Fang, and Qing Liao. 2022. Improving Multi-task Stance Detection with Multi-task Interaction Network. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 2990–3000, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.