  Document-level relation extraction aims to categorize the association between
any two entities within a document. We find that previous methods for
document-level relation extraction are ineffective in exploiting the full
potential of large amounts of training data with varied noise levels. For
example, in the ReDocRED benchmark dataset, state-of-the-art methods trained on
the large-scale, lower-quality, distantly supervised training data generally do
not perform better than those trained solely on the smaller, high-quality,
human-annotated training data. To unlock the full potential of large-scale
noisy training data for document-level relation extraction, we propose TTM-RE,
a novel approach that integrates a trainable memory module, known as the Token
Turing Machine, with a noisy-robust loss function that accounts for the
positive-unlabeled setting. Extensive experiments on ReDocRED, a benchmark
dataset for document-level relation extraction, reveal that TTM-RE achieves
state-of-the-art performance (with an absolute F1 score improvement of over
3%). Ablation studies further illustrate the superiority of TTM-RE in other
domains (the ChemDisGene dataset in the biomedical domain) and under highly
unlabeled settings.
