Improving Long-Tail Relation Extraction with Collaborating Relation-Augmented Attention

Yang Li, Tao Shen, Guodong Long, Jing Jiang, Tianyi Zhou, Chengqi Zhang


Abstract
Wrong labeling problem and long-tail relations are two main challenges caused by distant supervision in relation extraction. Recent works alleviate the wrong labeling by selective attention via multi-instance learning, but cannot well handle long-tail relations even if hierarchies of the relations are introduced to share knowledge. In this work, we propose a novel neural network, Collaborating Relation-augmented Attention (CoRA), to handle both the wrong labeling and long-tail relations. Particularly, we first propose relation-augmented attention network as base model. It operates on sentence bag with a sentence-to-relation attention to minimize the effect of wrong labeling. Then, facilitated by the proposed base model, we introduce collaborating relation features shared among relations in the hierarchies to promote the relation-augmenting process and balance the training data for long-tail relations. Besides the main training objective to predict the relation of a sentence bag, an auxiliary objective is utilized to guide the relation-augmenting process for a more accurate bag-level representation. In the experiments on the popular benchmark dataset NYT, the proposed CoRA improves the prior state-of-the-art performance by a large margin in terms of Precision@N, AUC and Hits@K. Further analyses verify its superior capability in handling long-tail relations in contrast to the competitors.
Anthology ID:
2020.coling-main.145
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:
1653–1664
Language:
URL:
https://aclanthology.org/2020.coling-main.145
DOI:
10.18653/v1/2020.coling-main.145
Bibkey:
Cite (ACL):
Yang Li, Tao Shen, Guodong Long, Jing Jiang, Tianyi Zhou, and Chengqi Zhang. 2020. Improving Long-Tail Relation Extraction with Collaborating Relation-Augmented Attention. In Proceedings of the 28th International Conference on Computational Linguistics, pages 1653–1664, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
Improving Long-Tail Relation Extraction with Collaborating Relation-Augmented Attention (Li et al., COLING 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.coling-main.145.pdf
Code
 YangLi1221/CoRA +  additional community code