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

Heyan Chai, Siyu Tang, Jinhao Cui, Ye Ding, Binxing Fang, Qing Liao


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.
Anthology ID:
2022.emnlp-main.193
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2990–3000
Language:
URL:
https://aclanthology.org/2022.emnlp-main.193
DOI:
Bibkey:
Cite (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.
Cite (Informal):
Improving Multi-task Stance Detection with Multi-task Interaction Network (Chai et al., EMNLP 2022)
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PDF:
https://preview.aclanthology.org/ingestion-script-update/2022.emnlp-main.193.pdf