Improving Sentence-Level Relation Extraction through Curriculum Learning

Understand

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.