Abstract
We present a neural network-based joint approach for emotion classification and emotion cause detection, which attempts to capture mutual benefits across the two sub-tasks of emotion analysis. Considering that emotion classification and emotion cause detection need different kinds of features (affective and event-based separately), we propose a joint encoder which uses a unified framework to extract features for both sub-tasks and a joint model trainer which simultaneously learns two models for the two sub-tasks separately. Our experiments on Chinese microblogs show that the joint approach is very promising.- Anthology ID:
- D18-1066
- 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:
- 646–651
- Language:
- URL:
- https://aclanthology.org/D18-1066
- DOI:
- 10.18653/v1/D18-1066
- Cite (ACL):
- Ying Chen, Wenjun Hou, Xiyao Cheng, and Shoushan Li. 2018. Joint Learning for Emotion Classification and Emotion Cause Detection. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 646–651, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Joint Learning for Emotion Classification and Emotion Cause Detection (Chen et al., EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/ingest-bitext-workshop/D18-1066.pdf