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Towards Zero and Few-shot Knowledge-seeking Turn Detection in Task-orientated Dialogue Systems
Di Jin · Shuyang Gao · Seokhwan Kim · Yang Liu · Dilek Hakkani-Tur

Most prior work on task-oriented dialogue systems is restricted to supporting domain APIs. However, users may have requests that are out of the scope of these APIs. This work focuses on identifying such user requests. Existing methods for this task mainly rely on fine-tuning pre-trained models on large annotated data. We propose a novel method, REDE, based on adaptive representation learning and density estimation. REDE can be applied to zero/few-shots cases, and quickly learn a high-performing detector that is comparable to the full-supervision setting with only a few shots by updating less than 3K parameters. We demonstrate REDE's competitive performance on DSTC9 Track 1 dataset and our newly collected test set.

Author Information

Di Jin (Amazon Alexa AI)
Shuyang Gao (Amazon)
Seokhwan Kim (Amazon Alexa AI)
Yang Liu (Amazon)

- 2004 Ph.d -2005-2018 professor at Univ. of Texas at Dallas - 2015-2016 visiting research scientist at Google research - 2016-2017 research scientist at Facebook - 2017-2019 Head of LAIX AI lab - 2019-present principal scientist at Amazon, Alexa AI

Dilek Hakkani-Tur (Amazon Alexa AI)

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