Improving Limited Labeled Dialogue State Tracking with Self-Supervision
EMNLP, pp. 4462-4472, 2020.
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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