# A Question-answering Based Framework for Relation Extraction Validation

Source: [A Question-answering Based Framework for Relation Extraction Validation](<https://www.sota2.com/research/paper/68fa69af1e209131cea235c6>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:05:39.232Z

Authors: Jiayang Cheng, Haiyun Jiang, Deqing Yang, Yanghua Xiao

Published: 2021-04-07T06:08:36.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2104.02934>)

[PDF](<https://arxiv.org/pdf/2104.02934.pdf>)