TSAM: A Two-Stream Attention Model for Causal Emotion Entailment

Duzhen Zhang, Zhen Yang, Fandong Meng, Xiuyi Chen, Jie Zhou


Abstract
Causal Emotion Entailment (CEE) aims to discover the potential causes behind an emotion in a conversational utterance. Previous works formalize CEE as independent utterance pair classification problems, with emotion and speaker information neglected. From a new perspective, this paper considers CEE in a joint framework. We classify multiple utterances synchronously to capture the correlations between utterances in a global view and propose a Two-Stream Attention Model (TSAM) to effectively model the speaker’s emotional influences in the conversational history. Specifically, the TSAM comprises three modules: Emotion Attention Network (EAN), Speaker Attention Network (SAN), and interaction module. The EAN and SAN incorporate emotion and speaker information in parallel, and the subsequent interaction module effectively interchanges relevant information between the EAN and SAN via a mutual BiAffine transformation. Extensive experimental results demonstrate that our model achieves new State-Of-The-Art (SOTA) performance and outperforms baselines remarkably.
Anthology ID:
2022.coling-1.588
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:
6762–6772
Language:
URL:
https://aclanthology.org/2022.coling-1.588
DOI:
Bibkey:
Cite (ACL):
Duzhen Zhang, Zhen Yang, Fandong Meng, Xiuyi Chen, and Jie Zhou. 2022. TSAM: A Two-Stream Attention Model for Causal Emotion Entailment. In Proceedings of the 29th International Conference on Computational Linguistics, pages 6762–6772, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
TSAM: A Two-Stream Attention Model for Causal Emotion Entailment (Zhang et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.588.pdf
Code
 bladedancer957/tsam
Data
RECCONXia and Ding, 2019