Topic-Driven and Knowledge-Aware Transformer for Dialogue Emotion Detection

Lixing Zhu, Gabriele Pergola, Lin Gui, Deyu Zhou, Yulan He


Abstract
Emotion detection in dialogues is challenging as it often requires the identification of thematic topics underlying a conversation, the relevant commonsense knowledge, and the intricate transition patterns between the affective states. In this paper, we propose a Topic-Driven Knowledge-Aware Transformer to handle the challenges above. We firstly design a topic-augmented language model (LM) with an additional layer specialized for topic detection. The topic-augmented LM is then combined with commonsense statements derived from a knowledge base based on the dialogue contextual information. Finally, a transformer-based encoder-decoder architecture fuses the topical and commonsense information, and performs the emotion label sequence prediction. The model has been experimented on four datasets in dialogue emotion detection, demonstrating its superiority empirically over the existing state-of-the-art approaches. Quantitative and qualitative results show that the model can discover topics which help in distinguishing emotion categories.
Anthology ID:
2021.acl-long.125
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1571–1582
Language:
URL:
https://aclanthology.org/2021.acl-long.125
DOI:
10.18653/v1/2021.acl-long.125
Bibkey:
Cite (ACL):
Lixing Zhu, Gabriele Pergola, Lin Gui, Deyu Zhou, and Yulan He. 2021. Topic-Driven and Knowledge-Aware Transformer for Dialogue Emotion Detection. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 1571–1582, Online. Association for Computational Linguistics.
Cite (Informal):
Topic-Driven and Knowledge-Aware Transformer for Dialogue Emotion Detection (Zhu et al., ACL-IJCNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-long.125.pdf
Video:
 https://aclanthology.org/2021.acl-long.125.mp4
Code
 something678/TodKat
Data
ConceptNetDailyDialogEmoryNLPIEMOCAPMELD