KEViN: A Knowledge Enhanced Validity and Novelty Classifier for Arguments

Ameer Saadat-Yazdi, Xue Li*, Sandrine Chausson, Vaishak Belle, Björn Ross, Jeff Z Pan, Nadin Kokciyan

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The ArgMining 2022 Shared Task is concerned with predicting the validity and novelty of an inference for a given premise and conclusion pair. We propose two feed-forward network based models (KEViN1 and KEViN2), which combine features generated from several pretrained transformers and the WikiData knowledge graph. The transformers are used to predict entailment and semantic similarity, while WikiData is used to provide a semantic measure between concepts in the premise-conclusion pair. Our proposed models show significant improvement over RoBERTa, with KEViN1 outperforming KEViN2 and obtaining second rank on both subtasks (A and B) of the ArgMining 2022 Shared Task.
Original languageEnglish
Title of host publicationProceedings of 9th Workshop on Argument Mining
EditorsGabriella Lapesa, Jodi Schneider, Yohan Jo, Sougata Saha
PublisherInternational Conference on Computational Linguistics
Pages104-110
Number of pages7
Publication statusPublished - 12 Oct 2022
EventThe 9th Workshop on Argument Mining, 2022 - Gyeongju, Korea, Democratic People's Republic of
Duration: 12 Oct 202217 Oct 2022
Conference number: 9

Publication series

NameInternational Conference on Computational Linguistics - Proceedings of 9th Workshop on Argument Mining
PublisherInternational Conference on Computational Linguistics
Number14
Volume29
ISSN (Electronic)2591-2093

Workshop

WorkshopThe 9th Workshop on Argument Mining, 2022
Abbreviated titleArgMining 2022
Country/TerritoryKorea, Democratic People's Republic of
CityGyeongju
Period12/10/2217/10/22

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