@inproceedings{lertvittayakumjorn-etal-2021-knowledge,
    title = "Knowledge-Driven Slot Constraints for Goal-Oriented Dialogue Systems",
    author = "Lertvittayakumjorn, Piyawat  and
      Bonadiman, Daniele  and
      Mansour, Saab",
    editor = "Toutanova, Kristina  and
      Rumshisky, Anna  and
      Zettlemoyer, Luke  and
      Hakkani-Tur, Dilek  and
      Beltagy, Iz  and
      Bethard, Steven  and
      Cotterell, Ryan  and
      Chakraborty, Tanmoy  and
      Zhou, Yichao",
    booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2021.naacl-main.266/",
    doi = "10.18653/v1/2021.naacl-main.266",
    pages = "3407--3419",
    abstract = "In goal-oriented dialogue systems, users provide information through slot values to achieve specific goals. Practically, some combinations of slot values can be invalid according to external knowledge. For example, a combination of {\textquotedblleft}cheese pizza{\textquotedblright} (a menu item) and {\textquotedblleft}oreo cookies{\textquotedblright} (a topping) from an input utterance {\textquotedblleft}Can I order a cheese pizza with oreo cookies on top?{\textquotedblright} exemplifies such invalid combinations according to the menu of a restaurant business. Traditional dialogue systems allow execution of validation rules as a post-processing step after slots have been filled which can lead to error accumulation. In this paper, we formalize knowledge-driven slot constraints and present a new task of constraint violation detection accompanied with benchmarking data. Then, we propose methods to integrate the external knowledge into the system and model constraint violation detection as an end-to-end classification task and compare it to the traditional rule-based pipeline approach. Experiments on two domains of the MultiDoGO dataset reveal challenges of constraint violation detection and sets the stage for future work and improvements."
}