Improving Limited Labeled Dialogue State Tracking with Self-Supervision

EMNLP, pp. 4462-4472, 2020.

Other Links: arxiv.org|dblp.uni-trier.de|academic.microsoft.com

Abstract:

Existing dialogue state tracking (DST) models require plenty of labeled data. However, collecting high-quality labels is costly, especially when the number of domains increases. In this paper, we address a practical DST problem that is rarely discussed, i.e., learning efficiently with limited labeled data. We present and investigate two...More

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