  Relation extraction is an important task in knowledge acquisition and text
understanding. Existing works mainly focus on improving relation extraction by
extracting effective features or designing reasonable model structures.
However, few works have focused on how to validate and correct the results
generated by the existing relation extraction models. We argue that validation
is an important and promising direction to further improve the performance of
relation extraction. In this paper, we explore the possibility of using
question answering as validation. Specifically, we propose a novel
question-answering based framework to validate the results from relation
extraction models. Our proposed framework can be easily applied to existing
relation classifiers without any additional information. We conduct extensive
experiments on the popular NYT dataset to evaluate the proposed framework, and
observe consistent improvements over five strong baselines.
