@inproceedings{ouyang-etal-2020-dialogue,
title = "Dialogue State Tracking with Explicit Slot Connection Modeling",
author = "Ouyang, Yawen and
Chen, Moxin and
Dai, Xinyu and
Zhao, Yinggong and
Huang, Shujian and
Chen, Jiajun",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.5/",
doi = "10.18653/v1/2020.acl-main.5",
pages = "34--40",
abstract = "Recent proposed approaches have made promising progress in dialogue state tracking (DST). However, in multi-domain scenarios, ellipsis and reference are frequently adopted by users to express values that have been mentioned by slots from other domains. To handle these phenomena, we propose a Dialogue State Tracking with Slot Connections (DST-SC) model to explicitly consider slot correlations across different domains. Given a target slot, the slot connecting mechanism in DST-SC can infer its source slot and copy the source slot value directly, thus significantly reducing the difficulty of learning and reasoning. Experimental results verify the benefits of explicit slot connection modeling, and our model achieves state-of-the-art performance on MultiWOZ 2.0 and MultiWOZ 2.1 datasets."
}
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<abstract>Recent proposed approaches have made promising progress in dialogue state tracking (DST). However, in multi-domain scenarios, ellipsis and reference are frequently adopted by users to express values that have been mentioned by slots from other domains. To handle these phenomena, we propose a Dialogue State Tracking with Slot Connections (DST-SC) model to explicitly consider slot correlations across different domains. Given a target slot, the slot connecting mechanism in DST-SC can infer its source slot and copy the source slot value directly, thus significantly reducing the difficulty of learning and reasoning. Experimental results verify the benefits of explicit slot connection modeling, and our model achieves state-of-the-art performance on MultiWOZ 2.0 and MultiWOZ 2.1 datasets.</abstract>
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%0 Conference Proceedings
%T Dialogue State Tracking with Explicit Slot Connection Modeling
%A Ouyang, Yawen
%A Chen, Moxin
%A Dai, Xinyu
%A Zhao, Yinggong
%A Huang, Shujian
%A Chen, Jiajun
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F ouyang-etal-2020-dialogue
%X Recent proposed approaches have made promising progress in dialogue state tracking (DST). However, in multi-domain scenarios, ellipsis and reference are frequently adopted by users to express values that have been mentioned by slots from other domains. To handle these phenomena, we propose a Dialogue State Tracking with Slot Connections (DST-SC) model to explicitly consider slot correlations across different domains. Given a target slot, the slot connecting mechanism in DST-SC can infer its source slot and copy the source slot value directly, thus significantly reducing the difficulty of learning and reasoning. Experimental results verify the benefits of explicit slot connection modeling, and our model achieves state-of-the-art performance on MultiWOZ 2.0 and MultiWOZ 2.1 datasets.
%R 10.18653/v1/2020.acl-main.5
%U https://aclanthology.org/2020.acl-main.5/
%U https://doi.org/10.18653/v1/2020.acl-main.5
%P 34-40
Markdown (Informal)
[Dialogue State Tracking with Explicit Slot Connection Modeling](https://aclanthology.org/2020.acl-main.5/) (Ouyang et al., ACL 2020)
ACL