Semi-Supervised Bootstrapping of Dialogue State Trackers for Task-Oriented Modelling

Bo-Hsiang Tseng, Marek Rei, Paweł Budzianowski, Richard Turner, Bill Byrne, Anna Korhonen

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Abstract
Dialogue systems benefit greatly from optimizing on detailed annotations, such as transcribed utterances, internal dialogue state representations and dialogue act labels. However, collecting these annotations is expensive and time-consuming, holding back development in the area of dialogue modelling. In this paper, we investigate semi-supervised learning methods that are able to reduce the amount of required intermediate labelling. We find that by leveraging un-annotated data instead, the amount of turn-level annotations of dialogue state can be significantly reduced when building a neural dialogue system. Our analysis on the MultiWOZ corpus, covering a range of domains and topics, finds that annotations can be reduced by up to 30% while maintaining equivalent system performance. We also describe and evaluate the first end-to-end dialogue model created for the MultiWOZ corpus.
Anthology ID:
D19-1125
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1273–1278
Language:
URL:
https://aclanthology.org/D19-1125
DOI:
10.18653/v1/D19-1125
Bibkey:
Cite (ACL):
Bo-Hsiang Tseng, Marek Rei, Paweł Budzianowski, Richard Turner, Bill Byrne, and Anna Korhonen. 2019. Semi-Supervised Bootstrapping of Dialogue State Trackers for Task-Oriented Modelling. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 1273–1278, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Semi-Supervised Bootstrapping of Dialogue State Trackers for Task-Oriented Modelling (Tseng et al., EMNLP-IJCNLP 2019)
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PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/D19-1125.pdf
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