Abstract
We present a dialogue generation model that directly captures the variability in possible responses to a given input, which reduces the ‘boring output’ issue of deterministic dialogue models. Experiments show that our model generates more diverse outputs than baseline models, and also generates more consistently acceptable output than sampling from a deterministic encoder-decoder model.- Anthology ID:
- E17-2029
- Volume:
- Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
- Month:
- April
- Year:
- 2017
- Address:
- Valencia, Spain
- Editors:
- Mirella Lapata, Phil Blunsom, Alexander Koller
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 182–187
- Language:
- URL:
- https://aclanthology.org/E17-2029
- DOI:
- Cite (ACL):
- Kris Cao and Stephen Clark. 2017. Latent Variable Dialogue Models and their Diversity. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 182–187, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- Latent Variable Dialogue Models and their Diversity (Cao & Clark, EACL 2017)
- PDF:
- https://preview.aclanthology.org/ml4al-ingestion/E17-2029.pdf