Abstract
Recent neural models for response generation show good results in terms of general responses. In real conversations, however, depending on the speaker/responder, similar utterances should require different responses. In this study, we attempt to consider individual user’s information in adjusting the notable sequence-to-sequence (seq2seq) model for more diverse, user-specific responses. We assume that we need user-specific features to adjust the response and we argue that some selected representative words from the users are suitable for this task. Furthermore, we prove that even for unseen or unknown users, our model can provide more diverse and interesting responses, while maintaining correlation with input utterances. Experimental results with human evaluation show that our model can generate more interesting responses than the popular seq2seqmodel and achieve higher relevance with input utterances than our baseline.- Anthology ID:
- W18-6510
- Volume:
- Proceedings of the 11th International Conference on Natural Language Generation
- Month:
- November
- Year:
- 2018
- Address:
- Tilburg University, The Netherlands
- Venue:
- INLG
- SIG:
- SIGGEN
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 89–98
- Language:
- URL:
- https://aclanthology.org/W18-6510
- DOI:
- 10.18653/v1/W18-6510
- Cite (ACL):
- Abdurrisyad Fikri, Hiroya Takamura, and Manabu Okumura. 2018. Stylistically User-Specific Generation. In Proceedings of the 11th International Conference on Natural Language Generation, pages 89–98, Tilburg University, The Netherlands. Association for Computational Linguistics.
- Cite (Informal):
- Stylistically User-Specific Generation (Fikri et al., INLG 2018)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/W18-6510.pdf