Modeling Appropriate Language in Argumentation
Timon Ziegenbein, Shahbaz Syed, Felix Lange, Martin Potthast, Henning Wachsmuth
Abstract
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.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 87b84df1-289c-46f7-92c5-61dfff5b8170Cited by top-tier papers4
- Let's discuss! Quality Dimensions and Annotated Datasets for Computational Argument Quality AssessmentRositsa V. Ivanova, Thomas Huber, Christina NiklausEMNLP 2024 · 2 citations
- Teaching LLMs Human-Like Editing of Inappropriate Argumentation via Reinforcement LearningTimon Ziegenbein, Maja Stahl, Henning WachsmuthACL 2026
- LLM-based Rewriting of Inappropriate Argumentation using Reinforcement Learning from Machine FeedbackTimon Ziegenbein, Gabriella Skitalinskaya, Alireza Bayat Makou, Henning WachsmuthACL 2024
- PerspectiveMod: A Perspectivist Resource for Deliberative ModerationEva Maria Vecchi, Neele Falk, Carlotta Quensel, Iman Jundi et al.EMNLP 2025
Builds on2
- DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding SharingPengcheng He, Jianfeng Gao, Weizhu ChenICLR 2023 · 394 citations
- Multitask Instruction-based Prompting for Fallacy RecognitionTariq Alhindi, Tuhin Chakrabarty, Elena Musi, Smaranda MuresanEMNLP 2022 · 16 citations
Related papers
- A Large-Scale Dataset for Argument Quality Ranking: Construction and AnalysisShai Gretz, Roni Friedman, Edo Cohen-Karlik, Assaf Toledo et al.AAAI 2020 · 148 citations
- Towards Argument Mining for Social Good: A SurveyEva Maria Vecchi, Neele Falk, Iman Jundi, Gabriella LapesaACL 2021
- Evaluation and Facilitation of Online Discussions in the LLM Era: A SurveyKaterina Korre, Dimitris Tsirmpas, Nikos Gkoumas, Emma Cabalé et al.EMNLP 2025
- Identifying the Human Values behind ArgumentsJohannes Kiesel, Milad Alshomary, Nicolas Handke, Xiaoni Cai et al.ACL 2022
- Efficient Pairwise Annotation of Argument QualityLukas Gienapp, Benno Stein, Matthias Hagen, Martin PotthastACL 2020 · 17 citations
