license:agpl-3.0 region:us
    This dataset is for evaluating the performance of intent classification systems in the
    presence of "out-of-scope" queries. By "out-of-scope", we mean queries that do not fall
    into any of the system-supported intent classes. Most datasets include only data that is
    "in-scope". Our dataset includes both in-scope and out-of-scope data. You might also know
    the term "out-of-scope" by other terms, including "out-of-domain" or "out-of-distribution".
    @inproceedings{larson-etal-2019-evaluation,
    title = "An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction",
    author = "Larson, Stefan  and
      Mahendran, Anish  and
      Peper, Joseph J.  and
      Clarke, Christopher  and
      Lee, Andrew  and
      Hill, Parker  and
      Kummerfeld, Jonathan K.  and
      Leach, Kevin  and
      Laurenzano, Michael A.  and
      Tang, Lingjia  and
      Mars, Jason",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    year = "2019",
    url = "https://www.aclweb.org/anthology/D19-1131"
}
---
license: agpl-3.0
---
