mlsanityData to 14 Oct 2026

A Question-answering Based Framework for Relation Extraction Validation

arXiv 2104.02934 ↗First posted 7 Apr 2021cs.CL cs.AI cs.LGcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 20 of its 33 references that are in the map. First posted on arXiv 7 Apr 2021.

Builds on

20 of 33 references.

References · 20

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019ALBERT: A Lite BERT for Self-supervised Learning of Language Representationscited by 2,790
  3. 2018Know What You Don't Know: Unanswerable Questions for SQuADcited by 1,554
  4. 2017Zero-Shot Relation Extraction via Reading Comprehensioncited by 426
  5. 2018FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluationcited by 254
  6. 2018Model Evaluation, Model Selection, and Algorithm Selection in Machine Learningcited by 157
  7. 2018Graph Convolution over Pruned Dependency Trees Improves Relation Extractioncited by 146
  8. 2019Entity-Relation Extraction as Multi-Turn Question Answeringcited by 96
  9. 2018Ultra-Fine Entity Typingcited by 87
  10. 2019Attention Guided Graph Convolutional Networks for Relation Extractioncited by 82
  11. 2018Joint Slot Filling and Intent Detection via Capsule Neural Networkscited by 73
  12. 2017Open-World Knowledge Graph Completioncited by 64
8 more
  1. 2018RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Informationcited by 47
  2. 2015Relation Classification via Recurrent Neural Networkcited by 46
  3. 2018Reinforcement Learning for Relation Classification from Noisy Datacited by 46
  4. 2017Relation Extraction : A Surveycited by 44
  5. 2013Connecting Language and Knowledge Bases with Embedding Models for Relation Extractioncited by 21
  6. 2017Learning with Noise: Enhance Distantly Supervised Relation Extraction with Dynamic Transition Matrixcited by 14
  7. 2018Multi-Level Structured Self-Attentions for Distantly Supervised Relation Extractioncited by 8
  8. 2018QA4IE: A Question Answering based Framework for Information Extractioncited by 3

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Cited by year

Citing papers per year: 2023: 3; 2024: 2.

5 papers.

  1. 2023StoryAnalogy: Deriving Story-level Analogies from Large Language Models to Unlock Analogical Understandingcited by 26
  2. 2024ECon: On the Detection and Resolution of Evidence Conflictscited by 18
  3. 2024EventGround: Narrative Reasoning by Grounding to Eventuality-centric Knowledge Graphscited by 9
  4. 2023Weakly-Supervised Questions for Zero-Shot Relation Extractioncited by 2
  5. 2023A Benchmark for Text Expansion: Datasets, Metrics, and Baselinescited by 0