Capturing Dialogue State Variable Dependencies with an Energy-based Neural Dialogue State Tracker

Anh Duong Trinh, Robert J. Ross, John D. Kelleher

Abstract
Dialogue state tracking requires the population and maintenance of a multi-slot frame representation of the dialogue state. Frequently, dialogue state tracking systems assume independence between slot values within a frame. In this paper we argue that treating the prediction of each slot value as an independent prediction task may ignore important associations between the slot values, and, consequently, we argue that treating dialogue state tracking as a structured prediction problem can help to improve dialogue state tracking performance. To support this argument, the research presented in this paper is structured into three stages: (i) analyzing variable dependencies in dialogue data; (ii) applying an energy-based methodology to model dialogue state tracking as a structured prediction task; and (iii) evaluating the impact of inter-slot relationships on model performance. Overall we demonstrate that modelling the associations between target slots with an energy-based formalism improves dialogue state tracking performance in a number of ways.
Anthology ID:
W19-5910
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:
75–84
Language:
URL:
https://aclanthology.org/W19-5910
DOI:
10.18653/v1/W19-5910
Bibkey:
Cite (ACL):
Anh Duong Trinh, Robert J. Ross, and John D. Kelleher. 2019. Capturing Dialogue State Variable Dependencies with an Energy-based Neural Dialogue State Tracker. In Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue, pages 75–84, Stockholm, Sweden. Association for Computational Linguistics.
Cite (Informal):
Capturing Dialogue State Variable Dependencies with an Energy-based Neural Dialogue State Tracker (Trinh et al., SIGDIAL 2019)
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PDF:
https://preview.aclanthology.org/nschneid-patch-2/W19-5910.pdf
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