Improving Multi-label Emotion Classification via Sentiment Classification with Dual Attention Transfer Network
Jianfei Yu, Luís Marujo, Jing Jiang, Pradeep Karuturi, William Brendel
Abstract
In this paper, we target at improving the performance of multi-label emotion classification with the help of sentiment classification. Specifically, we propose a new transfer learning architecture to divide the sentence representation into two different feature spaces, which are expected to respectively capture the general sentiment words and the other important emotion-specific words via a dual attention mechanism. Experimental results on two benchmark datasets demonstrate the effectiveness of our proposed method.- Anthology ID:
- D18-1137
- 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:
- 1097–1102
- Language:
- URL:
- https://aclanthology.org/D18-1137
- DOI:
- 10.18653/v1/D18-1137
- Cite (ACL):
- Jianfei Yu, Luís Marujo, Jing Jiang, Pradeep Karuturi, and William Brendel. 2018. Improving Multi-label Emotion Classification via Sentiment Classification with Dual Attention Transfer Network. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1097–1102, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Improving Multi-label Emotion Classification via Sentiment Classification with Dual Attention Transfer Network (Yu et al., EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/D18-1137.pdf