Abstract
We present the first complete spoken dialogue system driven by a multiimensional statistical dialogue manager. This framework has been shown to substantially reduce data needs by leveraging domain-independent dimensions, such as social obligations or feedback, which (as we show) can be transferred between domains. In this paper, we conduct a user study and show that the performance of a multi-dimensional system, which can be adapted from a source domain, is equivalent to that of a one-dimensional baseline, which can only be trained from scratch.- Anthology ID:
- W19-5945
- Volume:
- Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue
- Month:
- September
- Year:
- 2019
- Address:
- Stockholm, Sweden
- Editors:
- Satoshi Nakamura, Milica Gasic, Ingrid Zukerman, Gabriel Skantze, Mikio Nakano, Alexandros Papangelis, Stefan Ultes, Koichiro Yoshino
- Venue:
- SIGDIAL
- SIG:
- SIGDIAL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 392–398
- Language:
- URL:
- https://aclanthology.org/W19-5945
- DOI:
- 10.18653/v1/W19-5945
- Cite (ACL):
- Simon Keizer, Ondřej Dušek, Xingkun Liu, and Verena Rieser. 2019. User Evaluation of a Multi-dimensional Statistical Dialogue System. In Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue, pages 392–398, Stockholm, Sweden. Association for Computational Linguistics.
- Cite (Informal):
- User Evaluation of a Multi-dimensional Statistical Dialogue System (Keizer et al., SIGDIAL 2019)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/W19-5945.pdf
- Code
- skeizer/madrigal