mlsanityData to 7 Oct 2026

Modeling Appropriate Language in Argumentation

arXiv 2305.14935 ↗First posted 24 May 2023cs.CLcited by 1

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Abstract (arXiv)

Online discussion moderators must make ad-hoc decisions about whether the contributions of discussion participants are appropriate or should be removed to maintain civility. Existing research on offensive language and the resulting tools cover only one aspect among many involved in such decisions. The question of what is considered appropriate in a controversial discussion has not yet been systematically addressed. In this paper, we operationalize appropriate language in argumentation for the first time. In particular, we model appropriateness through the absence of flaws, grounded in research on argument quality assessment, especially in aspects from rhetoric. From these, we derive a new taxonomy of 14 dimensions that determine inappropriate language in online discussions. Building on three argument quality corpora, we then create a corpus of 2191 arguments annotated for the 14 dimensions. Empirical analyses support that the taxonomy covers the concept of appropriateness comprehensively, showing several plausible correlations with argument quality dimensions. Moreover, results of baseline approaches to assessing appropriateness suggest that all dimensions can be modeled computationally on the corpus.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 9 of its 27 references that are in the map. First posted on arXiv 24 May 2023.

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9 of 27 references.

References · 9

  1. 2021DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharingcited by 1,219
  2. 2020SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articlescited by 79
  3. 2018Before Name-calling: Dynamics and Triggers of Ad Hominem Fallacies in Web Argumentationcited by 32
  4. 2017Argotario: Computational Argumentation Meets Serious Gamescited by 29
  5. 2022Logical Fallacy Detectioncited by 25
  6. 2023Multitask Instruction-based Prompting for Fallacy Recognitioncited by 22
  7. 2020Rhetoric, Logic, and Dialectic: Advancing Theory-based Argument Quality Assessment in Natural Language Processingcited by 17
  8. 2020Intrinsic Quality Assessment of Argumentscited by 13
  9. 2020Creating a Domain-diverse Corpus for Theory-based Argument Quality Assessmentcited by 10

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