User Evaluation of a Multi-dimensional Statistical Dialogue System

Simon Keizer, Ondrej Dusek, Xingkun Liu, Verena Rieser

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

We present the first complete spoken dialogue system driven by a multi-dimensional 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.
Original languageEnglish
Title of host publicationProceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue
PublisherAssociation for Computational Linguistics
Pages392–398
Number of pages7
ISBN (Electronic)9781950737611
DOIs
Publication statusPublished - 2019
Event20th Annual SIGdial Meeting on Discourse and Dialogue 2019 - Stockholm, Sweden
Duration: 11 Sep 201913 Sep 2019

Conference

Conference20th Annual SIGdial Meeting on Discourse and Dialogue 2019
Country/TerritorySweden
CityStockholm
Period11/09/1913/09/19

Fingerprint

Dive into the research topics of 'User Evaluation of a Multi-dimensional Statistical Dialogue System'. Together they form a unique fingerprint.

Cite this