Mining, Assessing, and Improving Arguments in NLP and the Social Sciences

Research output: Chapter in book/report/conference proceedingConference contributionResearchpeer review

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  • University of Stuttgart
  • Université Côte d'Azur
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Details

Original languageEnglish
Title of host publicationProceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
Subtitle of host publicationTutorial Abstracts
EditorsRoman Klinger, Naozaki Okazaki, Nicoletta Calzolari, Min-Yen Kan
Place of PublicationTorino, Italia
Pages26-32
Number of pages7
ISBN (electronic)9781959429463
Publication statusPublished - May 2023
Event17th Conference of the European Chapter of the Associationfor Computational Linguistics, EACL 2023 - Dubrovnik, Croatia
Duration: 2 May 20234 May 2023

Abstract

Computational argumentation is an interdisciplinary research field, connecting Natural Language Processing (NLP) to other disciplines such as the social sciences. This tutorial will focus on a task that recently got into the center of attention in the community: argument quality assessment, that is, what makes an argument good or bad? We structure the tutorial along three main coordinates: (1) the notions of argument quality across disciplines (how do we recognize good and bad arguments?), (2) the modeling of subjectivity (who argues to whom; what are their beliefs?), and (3) the generation of improved arguments (what makes an argument better?). The tutorial highlights interdisciplinary aspects of the field, ranging from the collaboration of theory and practice (e.g., in NLP and social sciences), to approaching different types of linguistic structures (e.g., social media versus parliamentary texts), and facing the ethical issues involved (e.g., how to build applications for the social good). A key feature of this tutorial is its interactive nature: We will involve the participants in two annotation studies on the assessment and the improvement of quality, and we will encourage them to reflect on the challenges and potential of these tasks.

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Cite this

Mining, Assessing, and Improving Arguments in NLP and the Social Sciences. / Lapesa, Gabriella; Vecchi, Eva Maria; Villata, Serena et al.
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts. ed. / Roman Klinger; Naozaki Okazaki; Nicoletta Calzolari; Min-Yen Kan. Torino, Italia, 2023. p. 26-32.

Research output: Chapter in book/report/conference proceedingConference contributionResearchpeer review

Lapesa, G, Vecchi, EM, Villata, S & Wachsmuth, H 2023, Mining, Assessing, and Improving Arguments in NLP and the Social Sciences. in R Klinger, N Okazaki, N Calzolari & M-Y Kan (eds), Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts. Torino, Italia, pp. 26-32, 17th Conference of the European Chapter of the Associationfor Computational Linguistics, EACL 2023, Dubrovnik, Croatia, 2 May 2023. https://doi.org/10.18653/v1/2023.eacl-tutorials.1
Lapesa, G., Vecchi, E. M., Villata, S., & Wachsmuth, H. (2023). Mining, Assessing, and Improving Arguments in NLP and the Social Sciences. In R. Klinger, N. Okazaki, N. Calzolari, & M.-Y. Kan (Eds.), Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts (pp. 26-32). https://doi.org/10.18653/v1/2023.eacl-tutorials.1
Lapesa G, Vecchi EM, Villata S, Wachsmuth H. Mining, Assessing, and Improving Arguments in NLP and the Social Sciences. In Klinger R, Okazaki N, Calzolari N, Kan MY, editors, Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts. Torino, Italia. 2023. p. 26-32 doi: 10.18653/v1/2023.eacl-tutorials.1
Lapesa, Gabriella ; Vecchi, Eva Maria ; Villata, Serena et al. / Mining, Assessing, and Improving Arguments in NLP and the Social Sciences. Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts. editor / Roman Klinger ; Naozaki Okazaki ; Nicoletta Calzolari ; Min-Yen Kan. Torino, Italia, 2023. pp. 26-32
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