User Evaluation of a Multi-dimensional Statistical Dialogue System

Simon Keizer, Ondřej Dušek, Xingkun Liu, Verena Rieser


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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/W19-5945.pdf