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Discriminative Nearest Neighbor Few-Shot Intent Detection by Transferring Natural Language Inference
2020
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
unpublished
Intent detection is one of the core components of goal-oriented dialog systems, and detecting out-of-scope (OOS) intents is also a practically important skill. Few-shot learning is attracting much attention to mitigate data scarcity, but OOS detection becomes even more challenging. In this paper, we present a simple yet effective approach, discriminative nearest neighbor classification with deep self-attention. Unlike softmax classifiers, we leverage BERTstyle pairwise encoding to train a
doi:10.18653/v1/2020.emnlp-main.411
fatcat:ijzws2bzqbb43if6jb56n2q3q4