  Sentence-level relation extraction mainly aims to classify the relation
between two entities in a sentence. The sentence-level relation extraction
corpus often contains data that are difficult for the model to infer or noise
data. In this paper, we propose a curriculum learning-based relation extraction
model that splits data by difficulty and utilizes them for learning. In the
experiments with the representative sentence-level relation extraction
datasets, TACRED and Re-TACRED, the proposed method obtained an F1-score of
75.0% and 91.4% respectively, which are the state-of-the-art performance.
