Discriminative Reasoning for Document-level Relation Extraction

Findings (ACL) 2021  ·  Wang Xu, Kehai Chen, Tiejun Zhao ·

Document-level relation extraction (DocRE) models generally use graph networks to implicitly model the reasoning skill (i.e., pattern recognition, logical reasoning, coreference reasoning, etc.) related to the relation between one entity pair in a document. In this paper, we propose a novel discriminative reasoning framework to explicitly model the paths of these reasoning skills between each entity pair in this document. Thus, a discriminative reasoning network is designed to estimate the relation probability distribution of different reasoning paths based on the constructed graph and vectorized document contexts for each entity pair, thereby recognizing their relation. Experimental results show that our method outperforms the previous state-of-the-art performance on the large-scale DocRE dataset. The code is publicly available at https://github.com/xwjim/DRN.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Relation Extraction DocRED DRN-BERT-base F1 61.37 # 24
Ign F1 59.15 # 26
Relation Extraction DocRED DRN-GloVe F1 56.33 # 47
Ign F1 54.35 # 48


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