Contextualized Emotion Recognition in Conversation as Sequence Tagging

Yan Wang, Jiayu Zhang, Jun Ma, Shaojun Wang, Jing Xiao


Abstract
Emotion recognition in conversation (ERC) is an important topic for developing empathetic machines in a variety of areas including social opinion mining, health-care and so on. In this paper, we propose a method to model ERC task as sequence tagging where a Conditional Random Field (CRF) layer is leveraged to learn the emotional consistency in the conversation. We employ LSTM-based encoders that capture self and inter-speaker dependency of interlocutors to generate contextualized utterance representations which are fed into the CRF layer. For capturing long-range global context, we use a multi-layer Transformer encoder to enhance the LSTM-based encoder. Experiments show that our method benefits from modeling the emotional consistency and outperforms the current state-of-the-art methods on multiple emotion classification datasets.
Anthology ID:
2020.sigdial-1.23
Volume:
Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Month:
July
Year:
2020
Address:
1st virtual meeting
Venue:
SIGDIAL
SIG:
SIGDIAL
Publisher:
Association for Computational Linguistics
Note:
Pages:
186–195
Language:
URL:
https://aclanthology.org/2020.sigdial-1.23
DOI:
Bibkey:
Cite (ACL):
Yan Wang, Jiayu Zhang, Jun Ma, Shaojun Wang, and Jing Xiao. 2020. Contextualized Emotion Recognition in Conversation as Sequence Tagging. In Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 186–195, 1st virtual meeting. Association for Computational Linguistics.
Cite (Informal):
Contextualized Emotion Recognition in Conversation as Sequence Tagging (Wang et al., SIGDIAL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.sigdial-1.23.pdf
Video:
 https://youtube.com/watch?v=1PH6JXbc3EI
Data
DailyDialogIEMOCAPMELD