@inproceedings{ultes-maier-2020-similarity,
title = "Similarity Scoring for Dialogue Behaviour Comparison",
author = "Ultes, Stefan and
Maier, Wolfgang",
editor = "Pietquin, Olivier and
Muresan, Smaranda and
Chen, Vivian and
Kennington, Casey and
Vandyke, David and
Dethlefs, Nina and
Inoue, Koji and
Ekstedt, Erik and
Ultes, Stefan",
booktitle = "Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
month = jul,
year = "2020",
address = "1st virtual meeting",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.sigdial-1.38",
doi = "10.18653/v1/2020.sigdial-1.38",
pages = "311--322",
abstract = "The differences in decision making between behavioural models of voice interfaces are hard to capture using existing measures for the absolute performance of such models. For instance, two models may have a similar task success rate, but very different ways of getting there. In this paper, we propose a general methodology to compute the similarity of two dialogue behaviour models and investigate different ways of computing scores on both the semantic and the textual level. Complementing absolute measures of performance, we test our scores on three different tasks and show the practical usability of the measures.",
}
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<abstract>The differences in decision making between behavioural models of voice interfaces are hard to capture using existing measures for the absolute performance of such models. For instance, two models may have a similar task success rate, but very different ways of getting there. In this paper, we propose a general methodology to compute the similarity of two dialogue behaviour models and investigate different ways of computing scores on both the semantic and the textual level. Complementing absolute measures of performance, we test our scores on three different tasks and show the practical usability of the measures.</abstract>
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%0 Conference Proceedings
%T Similarity Scoring for Dialogue Behaviour Comparison
%A Ultes, Stefan
%A Maier, Wolfgang
%Y Pietquin, Olivier
%Y Muresan, Smaranda
%Y Chen, Vivian
%Y Kennington, Casey
%Y Vandyke, David
%Y Dethlefs, Nina
%Y Inoue, Koji
%Y Ekstedt, Erik
%Y Ultes, Stefan
%S Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2020
%8 July
%I Association for Computational Linguistics
%C 1st virtual meeting
%F ultes-maier-2020-similarity
%X The differences in decision making between behavioural models of voice interfaces are hard to capture using existing measures for the absolute performance of such models. For instance, two models may have a similar task success rate, but very different ways of getting there. In this paper, we propose a general methodology to compute the similarity of two dialogue behaviour models and investigate different ways of computing scores on both the semantic and the textual level. Complementing absolute measures of performance, we test our scores on three different tasks and show the practical usability of the measures.
%R 10.18653/v1/2020.sigdial-1.38
%U https://aclanthology.org/2020.sigdial-1.38
%U https://doi.org/10.18653/v1/2020.sigdial-1.38
%P 311-322
Markdown (Informal)
[Similarity Scoring for Dialogue Behaviour Comparison](https://aclanthology.org/2020.sigdial-1.38) (Ultes & Maier, SIGDIAL 2020)
ACL
- Stefan Ultes and Wolfgang Maier. 2020. Similarity Scoring for Dialogue Behaviour Comparison. In Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 311–322, 1st virtual meeting. Association for Computational Linguistics.