Learning Strategies for Robust Argument Mining: An Analysis of Variations in Language and Domain

Ramon Ruiz-Dolz, Chr Jr Chiu, Chung Chi Chen, Noriko Kando, Hsin Hsi Chen

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

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Abstract

Argument mining has typically been researched for specific corpora belonging to concrete languages and domains independently in each research work. Human argumentation, however, has domain- and language-dependent linguistic features that determine the content and structure of arguments. Also, when deploying argument mining systems in the wild, we might not be able to control some of these features. Therefore, an important aspect that has not been thoroughly investigated in the argument mining literature is the robustness of such systems to variations in language and domain. In this paper, we present a complete analysis across three different languages and three different domains that allow us to have a better understanding on how to leverage the scarce available corpora to design argument mining systems that are more robust to natural language variations.

Original languageEnglish
Title of host publicationProceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation
Subtitle of host publication(LREC-COLING 2024)
EditorsNicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
PublisherEuropean Language Resources Association (ELRA)
Pages10286-10292
Number of pages7
ISBN (Electronic)9782493814104
Publication statusPublished - May 2024
EventJoint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024 - Hybrid, Torino, Italy
Duration: 20 May 202425 May 2024
https://lrec-coling-2024.org/

Conference

ConferenceJoint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024
Country/TerritoryItaly
CityTorino
Period20/05/2425/05/24
Internet address

Keywords

  • argument mining
  • argument relation
  • cross-lingual

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computational Theory and Mathematics
  • Computer Science Applications

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