A Question-answering Based Framework for Relation Extraction Validation
This addresses a gap in relation extraction for knowledge acquisition by providing a validation method, though it is incremental as it builds on existing classifiers.
The paper tackles the problem of validating and correcting results from existing relation extraction models by proposing a question-answering based framework, achieving consistent improvements over five strong baselines on the NYT dataset.
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.