  Document-level relation extraction (DocRE) predicts relations for entity
pairs that rely on long-range context-dependent reasoning in a document. As a
typical multi-label classification problem, DocRE faces the challenge of
effectively distinguishing a small set of positive relations from the majority
of negative ones. This challenge becomes even more difficult to overcome when
there exists a significant number of annotation errors in the dataset. In this
work, we aim to achieve better integration of both the discriminability and
robustness for the DocRE problem. Specifically, we first design an effective
loss function to endow high discriminability to both probabilistic outputs and
internal representations. We innovatively customize entropy minimization and
supervised contrastive learning for the challenging multi-label and long-tailed
learning problems. To ameliorate the impact of label errors, we equipped our
method with a novel negative label sampling strategy to strengthen the model
robustness. In addition, we introduce two new data regimes to mimic more
realistic scenarios with annotation errors and evaluate our sampling strategy.
Experimental results verify the effectiveness of each component and show that
our method achieves new state-of-the-art results on the DocRED dataset, its
recently cleaned version, Re-DocRED, and the proposed data regimes.
