@inproceedings{zhu-etal-2019-sim,
title = "{SIM}: A Slot-Independent Neural Model for Dialogue State Tracking",
author = "Zhu, Chenguang and
Zeng, Michael and
Huang, Xuedong",
editor = "Nakamura, Satoshi and
Gasic, Milica and
Zukerman, Ingrid and
Skantze, Gabriel and
Nakano, Mikio and
Papangelis, Alexandros and
Ultes, Stefan and
Yoshino, Koichiro",
booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
month = sep,
year = "2019",
address = "Stockholm, Sweden",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-5905",
doi = "10.18653/v1/W19-5905",
pages = "40--45",
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.",
}
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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.</abstract>
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%0 Conference Proceedings
%T SIM: A Slot-Independent Neural Model for Dialogue State Tracking
%A Zhu, Chenguang
%A Zeng, Michael
%A Huang, Xuedong
%Y Nakamura, Satoshi
%Y Gasic, Milica
%Y Zukerman, Ingrid
%Y Skantze, Gabriel
%Y Nakano, Mikio
%Y Papangelis, Alexandros
%Y Ultes, Stefan
%Y Yoshino, Koichiro
%S Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue
%D 2019
%8 September
%I Association for Computational Linguistics
%C Stockholm, Sweden
%F zhu-etal-2019-sim
%X 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.
%R 10.18653/v1/W19-5905
%U https://aclanthology.org/W19-5905
%U https://doi.org/10.18653/v1/W19-5905
%P 40-45
Markdown (Informal)
[SIM: A Slot-Independent Neural Model for Dialogue State Tracking](https://aclanthology.org/W19-5905) (Zhu et al., SIGDIAL 2019)
ACL