Abstract
We investigate the impact of search strategies in neural dialogue modeling. We first compare two standard search algorithms, greedy and beam search, as well as our newly proposed iterative beam search which produces a more diverse set of candidate responses. We evaluate these strategies in realistic full conversations with humans and propose a model-based Bayesian calibration to address annotator bias. These conversations are analyzed using two automatic metrics: log-probabilities assigned by the model and utterance diversity. Our experiments reveal that better search algorithms lead to higher rated conversations. However, finding the optimal selection mechanism to choose from a more diverse set of candidates is still an open question.- Anthology ID:
- W19-8609
- Volume:
- Proceedings of the 12th International Conference on Natural Language Generation
- Month:
- October–November
- Year:
- 2019
- Address:
- Tokyo, Japan
- Editors:
- Kees van Deemter, Chenghua Lin, Hiroya Takamura
- Venue:
- INLG
- SIG:
- SIGGEN
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 76–87
- Language:
- URL:
- https://aclanthology.org/W19-8609
- DOI:
- 10.18653/v1/W19-8609
- Cite (ACL):
- Ilia Kulikov, Alexander Miller, Kyunghyun Cho, and Jason Weston. 2019. Importance of Search and Evaluation Strategies in Neural Dialogue Modeling. In Proceedings of the 12th International Conference on Natural Language Generation, pages 76–87, Tokyo, Japan. Association for Computational Linguistics.
- Cite (Informal):
- Importance of Search and Evaluation Strategies in Neural Dialogue Modeling (Kulikov et al., INLG 2019)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/W19-8609.pdf
- Data
- ConvAI2