MERIT: Minimal SupErvision Through Label Augmentation for Biomedical RelatIon ExTraction

Relation Extraction (RE) is an important task in extracting structured data from free biomedical text. Obtaining labeled data needed to train RE models in specialized domains such as biomedicine can be very expensive because it requires expert knowledge. Thus, it is often the case that RE models need to be trained from relatively small labeled data sets. Despite the recent advances in Natural Language Processing (NLP) approaches for RE, training accurate RE models from small labeled data is still an open challenge. In this paper, we propose MERIT, a simple and effective approach for label augmentation that automatically increases the size of labeled data while introducing a moderate labeling noise. We performed extensive experiments on three benchmarks biomedical RE data sets. The results demonstrate the effectiveness of MERIT compared to the baseline.