Yongkui Lai


2021

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Learning to Bridge Metric Spaces: Few-shot Joint Learning of Intent Detection and Slot Filling
Yutai Hou | Yongkui Lai | Cheng Chen | Wanxiang Che | Ting Liu
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

2020

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Few-shot Slot Tagging with Collapsed Dependency Transfer and Label-enhanced Task-adaptive Projection Network
Yutai Hou | Wanxiang Che | Yongkui Lai | Zhihan Zhou | Yijia Liu | Han Liu | Ting Liu
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

In this paper, we explore the slot tagging with only a few labeled support sentences (a.k.a. few-shot). Few-shot slot tagging faces a unique challenge compared to the other fewshot classification problems as it calls for modeling the dependencies between labels. But it is hard to apply previously learned label dependencies to an unseen domain, due to the discrepancy of label sets. To tackle this, we introduce a collapsed dependency transfer mechanism into the conditional random field (CRF) to transfer abstract label dependency patterns as transition scores. In the few-shot setting, the emission score of CRF can be calculated as a word’s similarity to the representation of each label. To calculate such similarity, we propose a Label-enhanced Task-Adaptive Projection Network (L-TapNet) based on the state-of-the-art few-shot classification model – TapNet, by leveraging label name semantics in representing labels. Experimental results show that our model significantly outperforms the strongest few-shot learning baseline by 14.64 F1 scores in the one-shot setting.