DREEAM: Guiding Attention with Evidence for Improving Document-Level Relation Extraction

17 Feb 2023  ·  Youmi Ma, An Wang, Naoaki Okazaki ·

Document-level relation extraction (DocRE) is the task of identifying all relations between each entity pair in a document. Evidence, defined as sentences containing clues for the relationship between an entity pair, has been shown to help DocRE systems focus on relevant texts, thus improving relation extraction. However, evidence retrieval (ER) in DocRE faces two major issues: high memory consumption and limited availability of annotations. This work aims at addressing these issues to improve the usage of ER in DocRE. First, we propose DREEAM, a memory-efficient approach that adopts evidence information as the supervisory signal, thereby guiding the attention modules of the DocRE system to assign high weights to evidence. Second, we propose a self-training strategy for DREEAM to learn ER from automatically-generated evidence on massive data without evidence annotations. Experimental results reveal that our approach exhibits state-of-the-art performance on the DocRED benchmark for both DocRE and ER. To the best of our knowledge, DREEAM is the first approach to employ ER self-training.

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Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Relation Extraction DocRED DREEAM F1 67.53 # 1
Ign F1 65.47 # 1
Relation Extraction ReDocRED DREEAM F1 80.73 # 1
Ign F1 79.66 # 1

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