Relation Extraction (RE) is the task of predicting a relation between a subject and object in a sentence, while knowledge graph link prediction (KGLP) infers a set of objects — O , given a subject and a relation from a knowledge graph. These two problems are closely intertwined: given a sentence consisting of a subject and object — o , a RE model estimates a relation that a KGLP model (together with the subject) may use to infer a set of objects — O — that contains o . In this paper, we leverage this insight by proposing a multi-task learning framework that enhances RE models by jointly training on both RE and KGLP tasks. We illustrate the generality of our approach by applying it on three existing RE methods and achieve consistent improvements across our benchmark datasets.
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