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:
- 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)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/C16-1153.pdf