| # An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction | |
| Repository that accompanies [An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction](https://www.aclweb.org/anthology/D19-1131/). | |
| ## FAQs | |
| ### 1. What are the relevant files? | |
| 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. | |
| ### 2. What is the name of the dataset? | |
| The dataset was not given a name in the original paper, but [others](https://arxiv.org/pdf/2003.04807.pdf) have called it `CLINC150`. | |
| ### 3. What is this dataset for? | |
| 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". | |
| ### 4. What language is the dataset in? | |
| All queries are in English. | |
| ### 5. How does your dataset/evaluation handle multi-intent queries? | |
| All samples/queries in our dataset are single-intent samples. We consider the problem of multi-intent classification to be future work. | |
| ### 6. How did you gather the dataset? | |
| 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. | |
| ## Citation | |
| 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" | |
| } | |
| ``` |