Abstract
Traditional neural language models tend to generate generic replies with poor logic and no emotion. In this paper, a syntactically constrained bidirectional-asynchronous approach for emotional conversation generation (E-SCBA) is proposed to address this issue. In our model, pre-generated emotion keywords and topic keywords are asynchronously introduced into the process of decoding. It is much different from most existing methods which generate replies from the first word to the last. Through experiments, the results indicate that our approach not only improves the diversity of replies, but gains a boost on both logic and emotion compared with baselines.- Anthology ID:
- D18-1071
- 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:
- 678–683
- Language:
- URL:
- https://aclanthology.org/D18-1071
- DOI:
- 10.18653/v1/D18-1071
- Cite (ACL):
- Jingyuan Li and Xiao Sun. 2018. A Syntactically Constrained Bidirectional-Asynchronous Approach for Emotional Conversation Generation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 678–683, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- A Syntactically Constrained Bidirectional-Asynchronous Approach for Emotional Conversation Generation (Li & Sun, EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/D18-1071.pdf