# Intrinsic Quality Assessment of Arguments

arXiv: https://arxiv.org/abs/2010.12473 · HTML: https://mlsanity.com/paper/2010.12473/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2020.

## Built on by (most cited first, 13 of 13)

- [Scientia Potentia Est -- On the Role of Knowledge in Computational Argumentation](https://mlsanity.com/paper/2107.00281/) (2021)
- [Learning From Revisions: Quality Assessment of Claims in Argumentation at Scale](https://mlsanity.com/paper/2101.10250/) (2021)
- [Large Language Models in Argument Mining: A Survey](https://mlsanity.com/paper/2506.16383/) (2025)
- [Which Side Are You On? A Multi-task Dataset for End-to-End Argument Summarisation and Evaluation](https://mlsanity.com/paper/2406.03151/) (2024)
- [Assessing the Sufficiency of Arguments through Conclusion Generation](https://mlsanity.com/paper/2110.13495/) (2021)
- [LLM-based Rewriting of Inappropriate Argumentation using Reinforcement Learning from Machine Feedback](https://mlsanity.com/paper/2406.03363/) (2024)
- [Towards a Perspectivist Turn in Argument Quality Assessment](https://mlsanity.com/paper/2502.14501/) (2025)
- [CASA: Causality-driven Argument Sufficiency Assessment](https://mlsanity.com/paper/2401.05249/) (2024)
- [Argument Quality Assessment in the Age of Instruction-Following Large Language Models](https://mlsanity.com/paper/2403.16084/) (2024)
- [Modeling Appropriate Language in Argumentation](https://mlsanity.com/paper/2305.14935/) (2023)
- [Debating for Better Reasoning: An Unsupervised Multimodal Approach](https://mlsanity.com/paper/2505.14627/) (2025)
- [Teaching LLMs Human-Like Editing of Inappropriate Argumentation via Reinforcement Learning](https://mlsanity.com/paper/2604.12770/) (2026)
- [GRASP: Deterministic argument ranking in interaction graphs](https://mlsanity.com/paper/2605.19141/) (2026)

## Abstract (arXiv)

Several quality dimensions of natural language arguments have been investigated. Some are likely to be reflected in linguistic features (e.g., an argument's arrangement), whereas others depend on context (e.g., relevance) or topic knowledge (e.g., acceptability). In this paper, we study the intrinsic computational assessment of 15 dimensions, i.e., only learning from an argument's text. In systematic experiments with eight feature types on an existing corpus, we observe moderate but significant learning success for most dimensions. Rhetorical quality seems hardest to assess, and subjectivity features turn out strong, although length bias in the corpus impedes full validity. We also find that human assessors differ more clearly to each other than to our approach.