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        <title>A Sequence-to-sequence Approach for Numerical Slot-filling Dialog Systems</title>
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            <title>Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue</title>
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            <namePart type="family">Pietquin</namePart>
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    <abstract>Dialog systems capable of filling slots with numerical values have wide applicability to many task-oriented applications. In this paper, we perform a particular case study on the “number_of_guests” slot-filling in hotel reservation domain, and propose two methods to improve current dialog system model on 1. numerical reasoning performance by training the model to predict arithmetic expressions, and 2. multi-turn question generation by introducing additional context slots. Furthermore, because the proposed methods are all based on an end-to-end trainable sequence-to-sequence (seq2seq) neural model, it is possible to achieve further performance improvement on increasing dialog logs in the future.</abstract>
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%0 Conference Proceedings
%T A Sequence-to-sequence Approach for Numerical Slot-filling Dialog Systems
%A Shi, Hongjie
%Y Pietquin, Olivier
%Y Muresan, Smaranda
%Y Chen, Vivian
%Y Kennington, Casey
%Y Vandyke, David
%Y Dethlefs, Nina
%Y Inoue, Koji
%Y Ekstedt, Erik
%Y Ultes, Stefan
%S Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2020
%8 July
%I Association for Computational Linguistics
%C 1st virtual meeting
%F shi-2020-sequence
%X Dialog systems capable of filling slots with numerical values have wide applicability to many task-oriented applications. In this paper, we perform a particular case study on the “number_of_guests” slot-filling in hotel reservation domain, and propose two methods to improve current dialog system model on 1. numerical reasoning performance by training the model to predict arithmetic expressions, and 2. multi-turn question generation by introducing additional context slots. Furthermore, because the proposed methods are all based on an end-to-end trainable sequence-to-sequence (seq2seq) neural model, it is possible to achieve further performance improvement on increasing dialog logs in the future.
%R 10.18653/v1/2020.sigdial-1.34
%U https://aclanthology.org/2020.sigdial-1.34
%U https://doi.org/10.18653/v1/2020.sigdial-1.34
%P 272-277

Markdown (Informal)

[A Sequence-to-sequence Approach for Numerical Slot-filling Dialog Systems](https://aclanthology.org/2020.sigdial-1.34) (Shi, SIGDIAL 2020)

• A Sequence-to-sequence Approach for Numerical Slot-filling Dialog Systems (Shi, SIGDIAL 2020)

ACL

• Hongjie Shi. 2020. A Sequence-to-sequence Approach for Numerical Slot-filling Dialog Systems. In Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 272–277, 1st virtual meeting. Association for Computational Linguistics.