A Two-phase Prototypical Network Model for Incremental Few-shot Relation Classification

Haopeng Ren, Yi Cai, Xiaofeng Chen, Guohua Wang, Qing Li

Abstract
Relation Classification (RC) plays an important role in natural language processing (NLP). Current conventional supervised and distantly supervised RC models always make a closed-world assumption which ignores the emergence of novel relations in open environment. To incrementally recognize the novel relations, current two solutions (i.e, re-training and lifelong learning) are designed but suffer from the lack of large-scale labeled data for novel relations. Meanwhile, prototypical network enjoys better performance on both fields of deep supervised learning and few-shot learning. However, it still suffers from the incompatible feature embedding problem when the novel relations come in. Motivated by them, we propose a two-phase prototypical network with prototype attention alignment and triplet loss to dynamically recognize the novel relations with a few support instances meanwhile without catastrophic forgetting. Extensive experiments are conducted to evaluate the effectiveness of our proposed model.
Anthology ID:
2020.coling-main.142
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Editors:
Donia Scott, Nuria Bel, Chengqing Zong
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
1618–1629
Language:
URL:
https://aclanthology.org/2020.coling-main.142
DOI:
10.18653/v1/2020.coling-main.142
Bibkey:
Cite (ACL):
Haopeng Ren, Yi Cai, Xiaofeng Chen, Guohua Wang, and Qing Li. 2020. A Two-phase Prototypical Network Model for Incremental Few-shot Relation Classification. In Proceedings of the 28th International Conference on Computational Linguistics, pages 1618–1629, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
A Two-phase Prototypical Network Model for Incremental Few-shot Relation Classification (Ren et al., COLING 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/landing_page/2020.coling-main.142.pdf
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FewRel
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