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Document-level relation extraction (DocRE) aims to extract relations of allentity pairs in a document. A key challenge in DocRE is the cost of annotatingsuch data which requires intensive human effort. Thus, we investigate the caseof DocRE in a low-resource setting, and we find that existing models trained onlow data overestimate the NA ("no relation") label, causing limitedperformance. In this work, we approach the problem from a calibrationperspective and propose PRiSM, which learns to adapt logits based on relationsemantic information. We evaluate our method on three DocRE datasets anddemonstrate that integrating existing models with PRiSM improves performance byas much as 26.38 F1 score, while the calibration error drops as much as 36times when trained with about 3% of data. The code is publicly available athttps://github.com/brightjade/PRiSM.

PRiSM: Enhancing Low-Resource Document-Level Relation Extraction with Relation-Aware Score Calibration