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 language | English (US) |
---|---|
Title of host publication | ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference |
Publisher | Association for Computational Linguistics (ACL) |
Pages | 1381-1393 |
Number of pages | 13 |
ISBN (Electronic) | 9781952148255 |
State | Published - 2020 |
Event | 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020 - Virtual, Online, United States Duration: Jul 5 2020 → Jul 10 2020 |
Publication series
Name | Proceedings of the Annual Meeting of the Association for Computational Linguistics |
---|---|
ISSN (Print) | 0736-587X |
Conference
Conference | 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020 |
---|---|
Country/Territory | United States |
City | Virtual, Online |
Period | 7/5/20 → 7/10/20 |
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