SIM: A Slot-Independent Neural Model for Dialogue State Tracking

Chenguang Zhu, Michael Zeng, Xuedong Huang

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Abstract
Dialogue state tracking is an important component in task-oriented dialogue systems to identify users’ goals and requests as a dialogue proceeds. However, as most previous models are dependent on dialogue slots, the model complexity soars when the number of slots increases. In this paper, we put forward a slot-independent neural model (SIM) to track dialogue states while keeping the model complexity invariant to the number of dialogue slots. The model utilizes attention mechanisms between user utterance and system actions. SIM achieves state-of-the-art results on WoZ and DSTC2 tasks, with only 20% of the model size of previous models.
Anthology ID:
W19-5905
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:
40–45
Language:
URL:
https://aclanthology.org/W19-5905
DOI:
10.18653/v1/W19-5905
Bibkey:
Cite (ACL):
Chenguang Zhu, Michael Zeng, and Xuedong Huang. 2019. SIM: A Slot-Independent Neural Model for Dialogue State Tracking. In Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue, pages 40–45, Stockholm, Sweden. Association for Computational Linguistics.
Cite (Informal):
SIM: A Slot-Independent Neural Model for Dialogue State Tracking (Zhu et al., SIGDIAL 2019)
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PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/W19-5905.pdf
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