Abstract
A neural conversation model is a promising approach to develop dialogue systems with the ability of chit-chat. It allows training a model in an end-to-end manner without complex rule design nor feature engineering. However, as a side effect, the neural model tends to generate safe but uninformative and insensitive responses like “OK” and “I don’t know.” Such replies are called generic responses and regarded as a critical problem for user-engagement of dialogue systems. For a more engaging chit-chat experience, we propose a neural conversation model that generates responsive and self-expressive replies. Specifically, our model generates domain-aware and sentiment-rich responses. Experiments empirically confirmed that our model outperformed the sequence-to-sequence model; 68.1% of our responses were domain-aware with sentiment polarities, which was only 2.7% for responses generated by the sequence-to-sequence model.- Anthology ID:
- W19-4116
- Volume:
- Proceedings of the First Workshop on NLP for Conversational AI
- Month:
- August
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Yun-Nung Chen, Tania Bedrax-Weiss, Dilek Hakkani-Tur, Anuj Kumar, Mike Lewis, Thang-Minh Luong, Pei-Hao Su, Tsung-Hsien Wen
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 139–149
- Language:
- URL:
- https://preview.aclanthology.org/icon-24-ingestion/W19-4116/
- DOI:
- 10.18653/v1/W19-4116
- Cite (ACL):
- Kozo Chikai, Junya Takayama, and Yuki Arase. 2019. Responsive and Self-Expressive Dialogue Generation. In Proceedings of the First Workshop on NLP for Conversational AI, pages 139–149, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Responsive and Self-Expressive Dialogue Generation (Chikai et al., ACL 2019)
- PDF:
- https://preview.aclanthology.org/icon-24-ingestion/W19-4116.pdf
- Code
- KChikai/Responsive-Dialogue-Generation