Task-oriented dialog systems need to know when a query falls outside their range of supported intents, but current text classification corpora only define label sets that cover every example. We introduce a new dataset that includes queries that are out-of-scope---i.e., queries that do not fall into any of the system's supported intents. This poses a new challenge because models cannot assume that every query at inference time belongs to a system-supported intent class. Our dataset also covers 150 intent classes over 10 domains, capturing the breadth that a production task-oriented agent must handle. We evaluate a range of benchmark classifiers on our dataset along with several different out-of-scope identification schemes. We find that while the classifiers perform well on in-scope intent classification, they struggle to identify out-of-scope queries. Our dataset and evaluation fill an important gap in the field, offering a way of more rigorously and realistically benchmarking text classification in task-driven dialog systems.
@article{arxiv.1909.02027,
title = {An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction},
author = {Stefan Larson and Anish Mahendran and Joseph J. Peper and Christopher Clarke and Andrew Lee and Parker Hill and Jonathan K. Kummerfeld and Kevin Leach and Michael A. Laurenzano and Lingjia Tang and Jason Mars},
journal= {arXiv preprint arXiv:1909.02027},
year = {2019}
}