Joint Alignment of Multi-Task Feature and Label Spaces for Emotion Cause Pair Extraction

Shunjie Chen, Xiaochuan Shi, Jingye Li, Shengqiong Wu, Hao Fei, Fei Li, Donghong Ji


Abstract
Emotion cause pair extraction (ECPE), as one of the derived subtasks of emotion cause analysis (ECA), shares rich inter-related features with emotion extraction (EE) and cause extraction (CE). Therefore EE and CE are frequently utilized as auxiliary tasks for better feature learning, modeled via multi-task learning (MTL) framework by prior works to achieve state-of-the-art (SoTA) ECPE results. However, existing MTL-based methods either fail to simultaneously model the specific features and the interactive feature in between, or suffer from the inconsistency of label prediction. In this work, we consider addressing the above challenges for improving ECPE by performing two alignment mechanisms with a novel Aˆ2Net model. We first propose a feature-task alignment to explicitly model the specific emotion-&cause-specific features and the shared interactive feature. Besides, an inter-task alignment is implemented, in which the label distance between the ECPE and the combinations of EE&CE are learned to be narrowed for better label consistency. Evaluations of benchmarks show that our methods outperform current best-performing systems on all ECA subtasks. Further analysis proves the importance of our proposed alignment mechanisms for the task.
Anthology ID:
2022.coling-1.606
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
6955–6965
Language:
URL:
https://aclanthology.org/2022.coling-1.606
DOI:
Bibkey:
Cite (ACL):
Shunjie Chen, Xiaochuan Shi, Jingye Li, Shengqiong Wu, Hao Fei, Fei Li, and Donghong Ji. 2022. Joint Alignment of Multi-Task Feature and Label Spaces for Emotion Cause Pair Extraction. In Proceedings of the 29th International Conference on Computational Linguistics, pages 6955–6965, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Joint Alignment of Multi-Task Feature and Label Spaces for Emotion Cause Pair Extraction (Chen et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.606.pdf
Code
 csj199813/a2net_ecpe
Data
Xia and Ding, 2019