Double Graph Based Reasoning for Document-level Relation Extraction

September 29, 2020 · Entered Twilight · 🏛 Conference on Empirical Methods in Natural Language Processing

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Authors Shuang Zeng, Runxin Xu, Baobao Chang, Lei Li arXiv ID 2009.13752 Category cs.CL: Computation & Language Cross-listed cs.AI, cs.LG Citations 266 Venue Conference on Empirical Methods in Natural Language Processing Repository https://github.com/DreamInvoker/GAIN ⭐ 147 Last Checked 1 month ago

Abstract

Document-level relation extraction aims to extract relations among entities within a document. Different from sentence-level relation extraction, it requires reasoning over multiple sentences across a document. In this paper, we propose Graph Aggregation-and-Inference Network (GAIN) featuring double graphs. GAIN first constructs a heterogeneous mention-level graph (hMG) to model complex interaction among different mentions across the document. It also constructs an entity-level graph (EG), based on which we propose a novel path reasoning mechanism to infer relations between entities. Experiments on the public dataset, DocRED, show GAIN achieves a significant performance improvement (2.85 on F1) over the previous state-of-the-art. Our code is available at https://github.com/DreamInvoker/GAIN .