Variational Cross-domain Natural Language Generation for Spoken Dialogue Systems
Bo-Hsiang Tseng, Florian Kreyssig, Paweł Budzianowski, Iñigo Casanueva, Yen-Chen Wu, Stefan Ultes, Milica Gašić
Abstract
Cross-domain natural language generation (NLG) is still a difficult task within spoken dialogue modelling. Given a semantic representation provided by the dialogue manager, the language generator should generate sentences that convey desired information. Traditional template-based generators can produce sentences with all necessary information, but these sentences are not sufficiently diverse. With RNN-based models, the diversity of the generated sentences can be high, however, in the process some information is lost. In this work, we improve an RNN-based generator by considering latent information at the sentence level during generation using conditional variational auto-encoder architecture. We demonstrate that our model outperforms the original RNN-based generator, while yielding highly diverse sentences. In addition, our model performs better when the training data is limited.- Anthology ID:
- W18-5039
- Volume:
- Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Editors:
- Kazunori Komatani, Diane Litman, Kai Yu, Alex Papangelis, Lawrence Cavedon, Mikio Nakano
- Venue:
- SIGDIAL
- SIG:
- SIGDIAL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 338–343
- Language:
- URL:
- https://aclanthology.org/W18-5039
- DOI:
- 10.18653/v1/W18-5039
- Cite (ACL):
- Bo-Hsiang Tseng, Florian Kreyssig, Paweł Budzianowski, Iñigo Casanueva, Yen-Chen Wu, Stefan Ultes, and Milica Gašić. 2018. Variational Cross-domain Natural Language Generation for Spoken Dialogue Systems. In Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue, pages 338–343, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Variational Cross-domain Natural Language Generation for Spoken Dialogue Systems (Tseng et al., SIGDIAL 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/W18-5039.pdf