Two training strategies for improving relation extraction over universal graph

Qin Dai, Naoya Inoue, Ryo Takahashi, Kentaro Inui

研究成果: Conference contribution

抄録

This paper explores how the Distantly Supervised Relation Extraction (DS-RE) can benefit from the use of a Universal Graph (UG), the combination of a Knowledge Graph (KG) and a large-scale text collection. A straightforward extension of a current state-of-the-art neural model for DS-RE with a UG may lead to degradation in performance. We first report that this degradation is associated with the difficulty in learning a UG and then propose two training strategies: (1) Path Type Adaptive Pretraining, which sequentially trains the model with different types of UG paths so as to prevent the reliance on a single type of UG path; and (2) Complexity Ranking Guided Attention mechanism, which restricts the attention span according to the complexity of a UG path so as to force the model to extract features not only from simple UG paths but also from complex ones. Experimental results on both biomedical and NYT10 datasets prove the robustness of our methods and achieve a new state-ofthe-art result on the NYT10 dataset. The code and datasets used in this paper are available at https://github.com/baodaiqin/ UGDSRE.

本文言語English
ホスト出版物のタイトルEACL 2021 - 16th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference
出版社Association for Computational Linguistics (ACL)
ページ3673-3684
ページ数12
ISBN(電子版)9781954085022
出版ステータスPublished - 2021
イベント16th Conference of the European Chapter of the Associationfor Computational Linguistics, EACL 2021 - Virtual, Online
継続期間: 2021 4 192021 4 23

出版物シリーズ

名前EACL 2021 - 16th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference

Conference

Conference16th Conference of the European Chapter of the Associationfor Computational Linguistics, EACL 2021
CityVirtual, Online
Period21/4/1921/4/23

ASJC Scopus subject areas

  • ソフトウェア
  • 計算理論と計算数学
  • 言語学および言語

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