@inproceedings{shi-2020-sequence,
    title = "A Sequence-to-sequence Approach for Numerical Slot-filling Dialog Systems",
    author = "Shi, Hongjie",
    editor = "Pietquin, Olivier  and
      Muresan, Smaranda  and
      Chen, Vivian  and
      Kennington, Casey  and
      Vandyke, David  and
      Dethlefs, Nina  and
      Inoue, Koji  and
      Ekstedt, Erik  and
      Ultes, Stefan",
    booktitle = "Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = jul,
    year = "2020",
    address = "1st virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2020.sigdial-1.34/",
    doi = "10.18653/v1/2020.sigdial-1.34",
    pages = "272--277",
    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."
}