Knowledge Aware Emotion Recognition in Textual Conversations via Multi-Task Incremental Transformer

Duzhen Zhang, Xiuyi Chen, Shuang Xu, Bo Xu


Abstract
Emotion recognition in textual conversations (ERTC) plays an important role in a wide range of applications, such as opinion mining, recommender systems, and so on. ERTC, however, is a challenging task. For one thing, speakers often rely on the context and commonsense knowledge to express emotions; for another, most utterances contain neutral emotion in conversations, as a result, the confusion between a few non-neutral utterances and much more neutral ones restrains the emotion recognition performance. In this paper, we propose a novel Knowledge Aware Incremental Transformer with Multi-task Learning (KAITML) to address these challenges. Firstly, we devise a dual-level graph attention mechanism to leverage commonsense knowledge, which augments the semantic information of the utterance. Then we apply the Incremental Transformer to encode multi-turn contextual utterances. Moreover, we are the first to introduce multi-task learning to alleviate the aforementioned confusion and thus further improve the emotion recognition performance. Extensive experimental results show that our KAITML model outperforms the state-of-the-art models across five benchmark datasets.
Anthology ID:
2020.coling-main.392
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Editors:
Donia Scott, Nuria Bel, Chengqing Zong
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
4429–4440
Language:
URL:
https://aclanthology.org/2020.coling-main.392
DOI:
10.18653/v1/2020.coling-main.392
Bibkey:
Cite (ACL):
Duzhen Zhang, Xiuyi Chen, Shuang Xu, and Bo Xu. 2020. Knowledge Aware Emotion Recognition in Textual Conversations via Multi-Task Incremental Transformer. In Proceedings of the 28th International Conference on Computational Linguistics, pages 4429–4440, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
Knowledge Aware Emotion Recognition in Textual Conversations via Multi-Task Incremental Transformer (Zhang et al., COLING 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.coling-main.392.pdf
Data
ConceptNetEmoryNLPIEMOCAPMELD