A Co-Attention Neural Network Model for Emotion Cause Analysis with Emotional Context Awareness

Xiangju Li, Kaisong Song, Shi Feng, Daling Wang, Yifei Zhang


Abstract
Emotion cause analysis has been a key topic in natural language processing. Existing methods ignore the contexts around the emotion word which can provide an emotion cause clue. Meanwhile, the clauses in a document play different roles on stimulating a certain emotion, depending on their content relevance. Therefore, we propose a co-attention neural network model for emotion cause analysis with emotional context awareness. The method encodes the clauses with a co-attention based bi-directional long short-term memory into high-level input representations, which are further fed into a convolutional layer for emotion cause analysis. Experimental results show that our approach outperforms the state-of-the-art baseline methods.
Anthology ID:
D18-1506
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Editors:
Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
4752–4757
Language:
URL:
https://aclanthology.org/D18-1506
DOI:
10.18653/v1/D18-1506
Bibkey:
Cite (ACL):
Xiangju Li, Kaisong Song, Shi Feng, Daling Wang, and Yifei Zhang. 2018. A Co-Attention Neural Network Model for Emotion Cause Analysis with Emotional Context Awareness. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4752–4757, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
A Co-Attention Neural Network Model for Emotion Cause Analysis with Emotional Context Awareness (Li et al., EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-1506.pdf
Video:
 https://aclanthology.org/D18-1506.mp4