@inproceedings{feng-etal-2020-none,
    title = "{\textquotedblleft}None of the Above{\textquotedblright}: Measure Uncertainty in Dialog Response Retrieval",
    author = "Feng, Yulan  and
      Mehri, Shikib  and
      Eskenazi, Maxine  and
      Zhao, Tiancheng",
    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://preview.aclanthology.org/add-emnlp-2024-awards/2020.acl-main.182/",
    doi = "10.18653/v1/2020.acl-main.182",
    pages = "2013--2020",
    abstract = "This paper discusses the importance of uncovering uncertainty in end-to-end dialog tasks and presents our experimental results on uncertainty classification on the processed Ubuntu Dialog Corpus. We show that instead of retraining models for this specific purpose, we can capture the original retrieval model`s underlying confidence concerning the best prediction using trivial additional computation."
}