Metamorphic Testing of Relation Extraction Models

Relation extraction (RE) is a fundamental NLP task that aims to identify relations between some entities regarding a given text. RE forms the basis for many advanced NLP tasks, such as question answering and text summarization, and thus its quality is critical to the relevant downstream applications...

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Main Authors: Yuhe Sun, Zuohua Ding, Hongyun Huang, Senhao Zou, Mingyue Jiang
Format: Article
Language:English
Published: MDPI AG 2023-02-01
Series:Algorithms
Subjects:
Online Access:https://www.mdpi.com/1999-4893/16/2/102
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author Yuhe Sun
Zuohua Ding
Hongyun Huang
Senhao Zou
Mingyue Jiang
author_facet Yuhe Sun
Zuohua Ding
Hongyun Huang
Senhao Zou
Mingyue Jiang
author_sort Yuhe Sun
collection DOAJ
description Relation extraction (RE) is a fundamental NLP task that aims to identify relations between some entities regarding a given text. RE forms the basis for many advanced NLP tasks, such as question answering and text summarization, and thus its quality is critical to the relevant downstream applications. However, evaluating the quality of RE models is non-trivial. On the one hand, obtaining ground truth labels for individual test inputs is tedious and even difficult. On the other hand, there is an increasing need to understand the characteristics of RE models in terms of various aspects. To mitigate these issues, this study proposes evaluating RE models by applying metamorphic testing (MT). A total of eight metamorphic relations (MRs) are identified based on three categories of transformation operations, namely replacement, swap, and combination. These MRs encode some expected properties of different aspects of RE. We further apply MT to three popular RE models. Our experiments reveal a large number of prediction failures in the subject RE models, confirming that MT is effective for evaluating RE models. Further analysis of the experimental results reveals the advantages and disadvantages of our subject models and also uncovers some typical issues of RE models.
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spelling doaj.art-9aad79944dff444897ae2dcbb82f05162023-11-16T18:37:47ZengMDPI AGAlgorithms1999-48932023-02-0116210210.3390/a16020102Metamorphic Testing of Relation Extraction ModelsYuhe Sun0Zuohua Ding1Hongyun Huang2Senhao Zou3Mingyue Jiang4School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, ChinaSchool of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, ChinaLibrary Multimedia Big Data Center, Zhejiang Sci-Tech University, Hangzhou 310018, ChinaSchool of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, ChinaSchool of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, ChinaRelation extraction (RE) is a fundamental NLP task that aims to identify relations between some entities regarding a given text. RE forms the basis for many advanced NLP tasks, such as question answering and text summarization, and thus its quality is critical to the relevant downstream applications. However, evaluating the quality of RE models is non-trivial. On the one hand, obtaining ground truth labels for individual test inputs is tedious and even difficult. On the other hand, there is an increasing need to understand the characteristics of RE models in terms of various aspects. To mitigate these issues, this study proposes evaluating RE models by applying metamorphic testing (MT). A total of eight metamorphic relations (MRs) are identified based on three categories of transformation operations, namely replacement, swap, and combination. These MRs encode some expected properties of different aspects of RE. We further apply MT to three popular RE models. Our experiments reveal a large number of prediction failures in the subject RE models, confirming that MT is effective for evaluating RE models. Further analysis of the experimental results reveals the advantages and disadvantages of our subject models and also uncovers some typical issues of RE models.https://www.mdpi.com/1999-4893/16/2/102relation extractionmetamorphic testingmetamorphic relationquality evaluationtesting and validation
spellingShingle Yuhe Sun
Zuohua Ding
Hongyun Huang
Senhao Zou
Mingyue Jiang
Metamorphic Testing of Relation Extraction Models
Algorithms
relation extraction
metamorphic testing
metamorphic relation
quality evaluation
testing and validation
title Metamorphic Testing of Relation Extraction Models
title_full Metamorphic Testing of Relation Extraction Models
title_fullStr Metamorphic Testing of Relation Extraction Models
title_full_unstemmed Metamorphic Testing of Relation Extraction Models
title_short Metamorphic Testing of Relation Extraction Models
title_sort metamorphic testing of relation extraction models
topic relation extraction
metamorphic testing
metamorphic relation
quality evaluation
testing and validation
url https://www.mdpi.com/1999-4893/16/2/102
work_keys_str_mv AT yuhesun metamorphictestingofrelationextractionmodels
AT zuohuading metamorphictestingofrelationextractionmodels
AT hongyunhuang metamorphictestingofrelationextractionmodels
AT senhaozou metamorphictestingofrelationextractionmodels
AT mingyuejiang metamorphictestingofrelationextractionmodels