SAIS: Supervising and Augmenting Intermediate Steps for Document-Level Relation Extraction

Stepping from sentence-level to document-level, the research on relation extraction (RE) confronts increasing text length and more complicated entity interactions. Consequently, it is more challenging to encode the key information sources--relevant contexts and entity types. However, existing methods only implicitly learn to model these critical information sources while being trained for RE. As a result, they suffer the problems of ineffective supervision and uninterpretable model predictions. In contrast, we propose to explicitly teach the model to capture relevant contexts and entity types by supervising and augmenting intermediate steps (SAIS) for RE. Based on a broad spectrum of carefully designed tasks, our proposed SAIS method not only extracts relations of better quality due to more effective supervision, but also retrieves the corresponding supporting evidence more accurately so as to enhance interpretability. By assessing model uncertainty, SAIS further boosts the performance via evidence-based data augmentation and ensemble inference while reducing the computational cost. Eventually, SAIS delivers state-of-the-art RE results on three benchmarks (DocRED, CDR, and GDA) and outperforms the runner-up by 5.04% relatively in F1 score in evidence retrieval on DocRED.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Relation Extraction CDR SAISORE+CR+ET-SciBERT F1 79 # 1
Relation Extraction DocRED SAIS-BERT-base F1 62.77 # 9
Ign F1 60.96 # 9
Relation Extraction DocRED SAIS-RoBERTa-large F1 65.11 # 3
Ign F1 63.44 # 3
Relation Extraction GDA SAISORE+CR+ET-SciBERT F1 87.1 # 1


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