RE-Matching: A Fine-Grained Semantic Matching Method for Zero-Shot Relation Extraction (2024)

Jun Zhao,WenYu Zhan,Xin Zhao,Qi Zhang,Tao Gui,Zhongyu Wei,Junzhe Wang,Minlong Peng,Mingming Sun

Abstract

Semantic matching is a mainstream paradigm of zero-shot relation extraction, which matches a given input with a corresponding label description. The entities in the input should exactly match their hypernyms in the description, while the irrelevant contexts should be ignored when matching. However, general matching methods lack explicit modeling of the above matching pattern. In this work, we propose a fine-grained semantic matching method tailored for zero-shot relation extraction. Guided by the above matching pattern, we decompose the sentence-level similarity score into the entity matching score and context matching score. Considering that not all contextual words contribute equally to the relation semantics, we design a context distillation module to reduce the negative impact of irrelevant components on context matching. Experimental results show that our method achieves higher matching accuracy and more than 10 times faster inference speed, compared with the state-of-the-art methods.

Export citation

BibTeX
MODS XML
Endnote
Preformatted

@inproceedings{zhao-etal-2023-matching, title = "{RE}-Matching: A Fine-Grained Semantic Matching Method for Zero-Shot Relation Extraction", author = "Zhao, Jun and Zhan, WenYu and Zhao, Xin and Zhang, Qi and Gui, Tao and Wei, Zhongyu and Wang, Junzhe and Peng, Minlong and Sun, Mingming", editor = "Rogers, Anna and Boyd-Graber, Jordan and Okazaki, Naoaki", booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.acl-long.369", doi = "10.18653/v1/2023.acl-long.369", pages = "6680--6691", abstract = "Semantic matching is a mainstream paradigm of zero-shot relation extraction, which matches a given input with a corresponding label description. The entities in the input should exactly match their hypernyms in the description, while the irrelevant contexts should be ignored when matching. However, general matching methods lack explicit modeling of the above matching pattern. In this work, we propose a fine-grained semantic matching method tailored for zero-shot relation extraction. Guided by the above matching pattern, we decompose the sentence-level similarity score into the entity matching score and context matching score. Considering that not all contextual words contribute equally to the relation semantics, we design a context distillation module to reduce the negative impact of irrelevant components on context matching. Experimental results show that our method achieves higher matching accuracy and more than 10 times faster inference speed, compared with the state-of-the-art methods.",}

Download as File

<?xml version="1.0" encoding="UTF-8"?><modsCollection xmlns="http://www.loc.gov/mods/v3"><mods ID="zhao-etal-2023-matching"> <titleInfo> <title>RE-Matching: A Fine-Grained Semantic Matching Method for Zero-Shot Relation Extraction</title> </titleInfo> <name type="personal"> <namePart type="given">Jun</namePart> <namePart type="family">Zhao</namePart> <role> <roleTerm authority="marcrelator" type="text">author</roleTerm> </role> </name> <name type="personal"> <namePart type="given">WenYu</namePart> <namePart type="family">Zhan</namePart> <role> <roleTerm authority="marcrelator" type="text">author</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Xin</namePart> <namePart type="family">Zhao</namePart> <role> <roleTerm authority="marcrelator" type="text">author</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Qi</namePart> <namePart type="family">Zhang</namePart> <role> <roleTerm authority="marcrelator" type="text">author</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Tao</namePart> <namePart type="family">Gui</namePart> <role> <roleTerm authority="marcrelator" type="text">author</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Zhongyu</namePart> <namePart type="family">Wei</namePart> <role> <roleTerm authority="marcrelator" type="text">author</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Junzhe</namePart> <namePart type="family">Wang</namePart> <role> <roleTerm authority="marcrelator" type="text">author</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Minlong</namePart> <namePart type="family">Peng</namePart> <role> <roleTerm authority="marcrelator" type="text">author</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Mingming</namePart> <namePart type="family">Sun</namePart> <role> <roleTerm authority="marcrelator" type="text">author</roleTerm> </role> </name> <originInfo> <dateIssued>2023-07</dateIssued> </originInfo> <typeOfResource>text</typeOfResource> <relatedItem type="host"> <titleInfo> <title>Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)</title> </titleInfo> <name type="personal"> <namePart type="given">Anna</namePart> <namePart type="family">Rogers</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Jordan</namePart> <namePart type="family">Boyd-Graber</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Naoaki</namePart> <namePart type="family">Okazaki</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <originInfo> <publisher>Association for Computational Linguistics</publisher> <place> <placeTerm type="text">Toronto, Canada</placeTerm> </place> </originInfo> <genre authority="marcgt">conference publication</genre> </relatedItem> <abstract>Semantic matching is a mainstream paradigm of zero-shot relation extraction, which matches a given input with a corresponding label description. The entities in the input should exactly match their hypernyms in the description, while the irrelevant contexts should be ignored when matching. However, general matching methods lack explicit modeling of the above matching pattern. In this work, we propose a fine-grained semantic matching method tailored for zero-shot relation extraction. Guided by the above matching pattern, we decompose the sentence-level similarity score into the entity matching score and context matching score. Considering that not all contextual words contribute equally to the relation semantics, we design a context distillation module to reduce the negative impact of irrelevant components on context matching. Experimental results show that our method achieves higher matching accuracy and more than 10 times faster inference speed, compared with the state-of-the-art methods.</abstract> <identifier type="citekey">zhao-etal-2023-matching</identifier> <identifier type="doi">10.18653/v1/2023.acl-long.369</identifier> <location> <url>https://aclanthology.org/2023.acl-long.369</url> </location> <part> <date>2023-07</date> <extent unit="page"> <start>6680</start> <end>6691</end> </extent> </part></mods></modsCollection>

Download as File

%0 Conference Proceedings%T RE-Matching: A Fine-Grained Semantic Matching Method for Zero-Shot Relation Extraction%A Zhao, Jun%A Zhan, WenYu%A Zhao, Xin%A Zhang, Qi%A Gui, Tao%A Wei, Zhongyu%A Wang, Junzhe%A Peng, Minlong%A Sun, Mingming%Y Rogers, Anna%Y Boyd-Graber, Jordan%Y Okazaki, Naoaki%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)%D 2023%8 July%I Association for Computational Linguistics%C Toronto, Canada%F zhao-etal-2023-matching%X Semantic matching is a mainstream paradigm of zero-shot relation extraction, which matches a given input with a corresponding label description. The entities in the input should exactly match their hypernyms in the description, while the irrelevant contexts should be ignored when matching. However, general matching methods lack explicit modeling of the above matching pattern. In this work, we propose a fine-grained semantic matching method tailored for zero-shot relation extraction. Guided by the above matching pattern, we decompose the sentence-level similarity score into the entity matching score and context matching score. Considering that not all contextual words contribute equally to the relation semantics, we design a context distillation module to reduce the negative impact of irrelevant components on context matching. Experimental results show that our method achieves higher matching accuracy and more than 10 times faster inference speed, compared with the state-of-the-art methods.%R 10.18653/v1/2023.acl-long.369%U https://aclanthology.org/2023.acl-long.369%U https://doi.org/10.18653/v1/2023.acl-long.369%P 6680-6691

Download as File

Markdown (Informal)

[RE-Matching: A Fine-Grained Semantic Matching Method for Zero-Shot Relation Extraction](https://aclanthology.org/2023.acl-long.369) (Zhao et al., ACL 2023)

RE-Matching: A Fine-Grained Semantic Matching Method for Zero-Shot Relation Extraction (Zhao et al., ACL 2023)

ACL

Jun Zhao, WenYu Zhan, Xin Zhao, Qi Zhang, Tao Gui, Zhongyu Wei, Junzhe Wang, Minlong Peng, and Mingming Sun. 2023. RE-Matching: A Fine-Grained Semantic Matching Method for Zero-Shot Relation Extraction. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6680–6691, Toronto, Canada. Association for Computational Linguistics.