mlsanityData to 7 Oct 2026

Scientia Potentia Est -- On the Role of Knowledge in Computational Argumentation

arXiv 2107.00281 ↗First posted 1 Jul 2021cs.CLcited by 17

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Abstract (arXiv)

Despite extensive research efforts in recent years, computational argumentation (CA) remains one of the most challenging areas of natural language processing. The reason for this is the inherent complexity of the cognitive processes behind human argumentation, which integrate a plethora of different types of knowledge, ranging from topic-specific facts and common sense to rhetorical knowledge. The integration of knowledge from such a wide range in CA requires modeling capabilities far beyond many other natural language understanding tasks. Existing research on mining, assessing, reasoning over, and generating arguments largely acknowledges that much more knowledge is needed to accurately model argumentation computationally. However, a systematic overview of the types of knowledge introduced in existing CA models is missing, hindering targeted progress in the field. Adopting the operational definition of knowledge as any task-relevant normative information not provided as input, the survey paper at hand fills this gap by (1) proposing a taxonomy of types of knowledge required in CA tasks, (2) systematizing the large body of CA work according to the reliance on and exploitation of these knowledge types for the four main research areas in CA, and (3) outlining and discussing directions for future research efforts in CA.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 59 of its 237 references that are in the map. First posted on arXiv 1 Jul 2021.

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59 of 237 references.

References · 59

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38 more
  1. 2017Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluationcited by 206
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  5. 2019A Large-scale Dataset for Argument Quality Ranking: Construction and Analysiscited by 66
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 4; 2023: 4; 2024: 3; 2025: 3; 2026: 3.

17 papers.

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  1. 2023Sensitivity, Performance, Robustness: Deconstructing the Effect of Sociodemographic Promptingcited by 80
  2. 2022Fair and Argumentative Language Modeling for Computational Argumentationcited by 18
  3. 2025Evaluation and Facilitation of Online Discussions in the LLM Era: A Surveycited by 8
  4. 2022Contextual information integration for stance detection via cross-attentioncited by 6
  5. 2023Similarity-weighted Construction of Contextualized Commonsense Knowledge Graphs for Knowledge-intense Argumentation Taskscited by 6
  6. 2024A Hybrid Intelligence Method for Argument Miningcited by 6
  7. 2022Diversity Over Size: On the Effect of Sample and Topic Sizes for Topic-Dependent Argument Mining Datasetscited by 4
  8. 2023Contextualizing Argument Quality Assessment with Relevant Knowledgecited by 4
  9. 2023How to Handle Different Types of Out-of-Distribution Scenarios in Computational Argumentation? A Comprehensive and Fine-Grained Field Studycited by 4
  10. 2024Argument Quality Assessment in the Age of Instruction-Following Large Language Modelscited by 4
  11. 2025ArgInstruct: Specialized Instruction Fine-Tuning for Computational Argumentationcited by 4
  12. 2025Generalizability of Media Frames: Corpus creation and analysis across countriescited by 4
5 more
  1. 2026ArgBench: Benchmarking LLMs on Computational Argumentation Taskscited by 1
  2. 2026Structuring the Space of Perspectivescited by 1
  3. 2022Perturbations and Subpopulations for Testing Robustness in Token-Based Argument Unit Recognitioncited by 0
  4. 2024Persona Knowledge-Aligned Prompt Tuning Method for Online Debatecited by 0
  5. 2026Designing and Analysing Argument Mining Pipelines: Towards a Comprehensive Assessmentcited by 0