Improving Sentence-Level Relation Extraction through Curriculum Learning

Seongsik Park, Harksoo Kim

The sentence-level relation extraction mainly aims to classify the relation between two entities in a sentence. The sentence-level relation extraction corpus is often containing data of difficulty for the model to infer or noise data. In this paper, we propose a curriculum learning-based relation extraction model that split data by difficulty and utilize it for learning. In the experiments with the representative sentence-level relation extraction datasets, TACRED and Re-TACRED, the proposed method showed good performances.

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