  Relation extraction (RE) aims to predict a relation between a subject and an
object in a sentence, while knowledge graph link prediction (KGLP) aims to
predict a set of objects, O, given a subject and a relation from a knowledge
graph. These two problems are closely related as their respective objectives
are intertwined: given a sentence containing a subject and an object o, a RE
model predicts a relation that can then be used by a KGLP model together with
the subject, to predict a set of objects O. Thus, we expect object o to be in
set O. In this paper, we leverage this insight by proposing a multi-task
learning approach that improves the performance of RE models by jointly
training on RE and KGLP tasks. We illustrate the generality of our approach by
applying it on several existing RE models and empirically demonstrate how it
helps them achieve consistent performance gains.
