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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

139 Scopus citations

Abstract

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 few-shot 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.

Original languageEnglish (US)
Title of host publicationACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
PublisherAssociation for Computational Linguistics (ACL)
Pages1381-1393
Number of pages13
ISBN (Electronic)9781952148255
StatePublished - 2020
Event58th Annual Meeting of the Association for Computational Linguistics, ACL 2020 - Virtual, Online, United States
Duration: Jul 5 2020Jul 10 2020

Publication series

NameProceedings of the Annual Meeting of the Association for Computational Linguistics
ISSN (Print)0736-587X

Conference

Conference58th Annual Meeting of the Association for Computational Linguistics, ACL 2020
Country/TerritoryUnited States
CityVirtual, Online
Period7/5/207/10/20

Funding

We sincerely thank Ning Wang and Jiafeng Mao for the help on both paper and experiments. We are grateful for the helpful comments and suggestions from the anonymous reviewers. This work was supported by the National Natural Science Foundation of China (NSFC) via grant 61976072, 61632011 and 61772153.

ASJC Scopus subject areas

  • Computer Science Applications
  • Linguistics and Language
  • Language and Linguistics

Fingerprint

Dive into the research topics of 'Few-shot slot tagging with collapsed dependency transfer and label-enhanced task-adaptive projection network'. Together they form a unique fingerprint.

Cite this