A Bilingual Attention Network for Code-switched Emotion Prediction

Zhongqing Wang, Yue Zhang, Sophia Lee, Shoushan Li, Guodong Zhou


Abstract
Emotions in code-switching text can be expressed in either monolingual or bilingual forms. However, relatively little research has emphasized on code-switching text. In this paper, we propose a Bilingual Attention Network (BAN) model to aggregate the monolingual and bilingual informative words to form vectors from the document representation, and integrate the attention vectors to predict the emotion. The experiments show that the effectiveness of the proposed model. Visualization of the attention layers illustrates that the model selects qualitatively informative words.
Anthology ID:
C16-1153
Volume:
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
Month:
December
Year:
2016
Address:
Osaka, Japan
Editors:
Yuji Matsumoto, Rashmi Prasad
Venue:
COLING
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
1624–1634
Language:
URL:
https://aclanthology.org/C16-1153
DOI:
Bibkey:
Cite (ACL):
Zhongqing Wang, Yue Zhang, Sophia Lee, Shoushan Li, and Guodong Zhou. 2016. A Bilingual Attention Network for Code-switched Emotion Prediction. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1624–1634, Osaka, Japan. The COLING 2016 Organizing Committee.
Cite (Informal):
A Bilingual Attention Network for Code-switched Emotion Prediction (Wang et al., COLING 2016)
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PDF:
https://aclanthology.org/C16-1153.pdf