@inproceedings{fangchao-etal-2021-learning,
title = "From Learning-to-Match to Learning-to-Discriminate:Global Prototype Learning for Few-shot Relation Classification",
author = "Fangchao, Liu and
Xinyan, Xiao and
Lingyong, Yan and
Hongyu, Lin and
Xianpei, Han and
Dai, Dai and
Hua, Wu and
Le, Sun",
editor = "Li, Sheng and
Sun, Maosong and
Liu, Yang and
Wu, Hua and
Liu, Kang and
Che, Wanxiang and
He, Shizhu and
Rao, Gaoqi",
booktitle = "Proceedings of the 20th Chinese National Conference on Computational Linguistics",
month = aug,
year = "2021",
address = "Huhhot, China",
publisher = "Chinese Information Processing Society of China",
url = "https://aclanthology.org/2021.ccl-1.90",
pages = "1012--1023",
abstract = "Few-shot relation classification has attracted great attention recently and is regarded as an ef-fective way to tackle the long-tail problem in relation classification. Most previous works onfew-shot relation classification are based on learning-to-match paradigms which focus on learn-ing an effective universal matcher between the query and one target class prototype based oninner-class support sets. However the learning-to-match paradigm focuses on capturing the sim-ilarity knowledge between query and class prototype while fails to consider discriminative infor-mation between different candidate classes. Such information is critical especially when targetclasses are highly confusing and domain shifting exists between training and testing phases. Inthis paper we propose the Global Transformed Prototypical Networks(GTPN) which learns tobuild a few-shot model to directly discriminate between the query and all target classes with bothinner-class local information and inter-class global information. Such learning-to-discriminate paradigm can make the model concentrate more on the discriminative knowledge between allcandidate classes and therefore leads to better classification performance. We conducted exper-iments on standard FewRel benchmarks. Experimental results show that GTPN achieves very competitive performance on few-shot relation classification and reached the best performance onthe official leaderboard of FewRel 2.0 1.",
language = "English",
}
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<abstract>Few-shot relation classification has attracted great attention recently and is regarded as an ef-fective way to tackle the long-tail problem in relation classification. Most previous works onfew-shot relation classification are based on learning-to-match paradigms which focus on learn-ing an effective universal matcher between the query and one target class prototype based oninner-class support sets. However the learning-to-match paradigm focuses on capturing the sim-ilarity knowledge between query and class prototype while fails to consider discriminative infor-mation between different candidate classes. Such information is critical especially when targetclasses are highly confusing and domain shifting exists between training and testing phases. Inthis paper we propose the Global Transformed Prototypical Networks(GTPN) which learns tobuild a few-shot model to directly discriminate between the query and all target classes with bothinner-class local information and inter-class global information. Such learning-to-discriminate paradigm can make the model concentrate more on the discriminative knowledge between allcandidate classes and therefore leads to better classification performance. We conducted exper-iments on standard FewRel benchmarks. Experimental results show that GTPN achieves very competitive performance on few-shot relation classification and reached the best performance onthe official leaderboard of FewRel 2.0 1.</abstract>
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%0 Conference Proceedings
%T From Learning-to-Match to Learning-to-Discriminate:Global Prototype Learning for Few-shot Relation Classification
%A Fangchao, Liu
%A Xinyan, Xiao
%A Lingyong, Yan
%A Hongyu, Lin
%A Xianpei, Han
%A Dai, Dai
%A Hua, Wu
%A Le, Sun
%Y Li, Sheng
%Y Sun, Maosong
%Y Liu, Yang
%Y Wu, Hua
%Y Liu, Kang
%Y Che, Wanxiang
%Y He, Shizhu
%Y Rao, Gaoqi
%S Proceedings of the 20th Chinese National Conference on Computational Linguistics
%D 2021
%8 August
%I Chinese Information Processing Society of China
%C Huhhot, China
%G English
%F fangchao-etal-2021-learning
%X Few-shot relation classification has attracted great attention recently and is regarded as an ef-fective way to tackle the long-tail problem in relation classification. Most previous works onfew-shot relation classification are based on learning-to-match paradigms which focus on learn-ing an effective universal matcher between the query and one target class prototype based oninner-class support sets. However the learning-to-match paradigm focuses on capturing the sim-ilarity knowledge between query and class prototype while fails to consider discriminative infor-mation between different candidate classes. Such information is critical especially when targetclasses are highly confusing and domain shifting exists between training and testing phases. Inthis paper we propose the Global Transformed Prototypical Networks(GTPN) which learns tobuild a few-shot model to directly discriminate between the query and all target classes with bothinner-class local information and inter-class global information. Such learning-to-discriminate paradigm can make the model concentrate more on the discriminative knowledge between allcandidate classes and therefore leads to better classification performance. We conducted exper-iments on standard FewRel benchmarks. Experimental results show that GTPN achieves very competitive performance on few-shot relation classification and reached the best performance onthe official leaderboard of FewRel 2.0 1.
%U https://aclanthology.org/2021.ccl-1.90
%P 1012-1023
Markdown (Informal)
[From Learning-to-Match to Learning-to-Discriminate:Global Prototype Learning for Few-shot Relation Classification](https://aclanthology.org/2021.ccl-1.90) (Fangchao et al., CCL 2021)
ACL
- Liu Fangchao, Xiao Xinyan, Yan Lingyong, Lin Hongyu, Han Xianpei, Dai Dai, Wu Hua, and Sun Le. 2021. From Learning-to-Match to Learning-to-Discriminate:Global Prototype Learning for Few-shot Relation Classification. In Proceedings of the 20th Chinese National Conference on Computational Linguistics, pages 1012–1023, Huhhot, China. Chinese Information Processing Society of China.