Repository that accompanies An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction.

See data/data_full.json for the "full" dataset. This is the dataset used in Table 1 (the "Full" columns). This file contains 150 "in-scope" intent classes, each with 100 train, 20 validation, and 30 test samples. There are 100 train and validation out-of-scope samples, and 1000 out-of-scope test samples.

The dataset was not given a name in the original paper, but others have called it CLINC150.

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".

All queries are in English.

All samples/queries in our dataset are single-intent samples. We consider the problem of multi-intent classification to be future work.

We used crowdsourcing to generate the dataset. We asked crowd workers to either paraphrase "seed" phrases, or respond to scenarios (e.g. "pretend you need to book a flight, what would you say?"). We used crowdsourcing to generate data for both in-scope and out-of-scope data.

If you find our dataset useful, please be sure to cite:

@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"
}