@inproceedings{khayrallah-sedoc-2021-measuring,
    title = "Measuring the {\textquoteleft}{I} don`t know' Problem through the Lens of {G}ricean Quantity",
    author = "Khayrallah, Huda  and
      Sedoc, Jo{\~a}o",
    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/Author-page-Marten-During-lu/2021.naacl-main.450/",
    doi = "10.18653/v1/2021.naacl-main.450",
    pages = "5659--5670",
    abstract = "We consider the intrinsic evaluation of neural generative dialog models through the lens of Grice`s Maxims of Conversation (1975). Based on the maxim of Quantity (be informative), we propose Relative Utterance Quantity (RUQ) to diagnose the {\textquoteleft}I don`t know' problem, in which a dialog system produces generic responses. The linguistically motivated RUQ diagnostic compares the model score of a generic response to that of the reference response. We find that for reasonable baseline models, {\textquoteleft}I don`t know' is preferred over the reference the majority of the time, but this can be reduced to less than 5{\%} with hyperparameter tuning. RUQ allows for the direct analysis of the {\textquoteleft}I don`t know' problem, which has been addressed but not analyzed by prior work."
}