mlsanityData to 7 Oct 2026

An Axiomatic Study of the Evaluation of Enthymeme Decoding in Weighted Structured Argumentation

arXiv 2411.04555 ↗First posted 7 Nov 2024cs.AI cs.LOcited by 1

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Abstract (arXiv)

An argument can be seen as a pair consisting of a set of premises and a claim supported by them. Arguments used by humans are often enthymemes, i.e., some premises are implicit. To better understand, evaluate, and compare enthymemes, it is essential to decode them, i.e., to find the missing premisses. Many enthymeme decodings are possible. We need to distinguish between reasonable decodings and unreasonable ones. However, there is currently no research in the literature on "How to evaluate decodings?". To pave the way and achieve this goal, we introduce seven criteria related to decoding, based on different research areas. Then, we introduce the notion of criterion measure, the objective of which is to evaluate a decoding with regard to a certain criterion. Since such measures need to be validated, we introduce several desirable properties for them, called axioms. Another main contribution of the paper is the construction of certain criterion measures that are validated by our axioms. Such measures can be used to identify the best enthymemes decodings.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 5 of its 35 references that are in the map. First posted on arXiv 7 Nov 2024.

Builds on

5 of 35 references.

References · 5

  1. 2019Explainability Fact Sheets: A Framework for Systematic Assessment of Explainable Approachescited by 101
  2. 2017The Argument Reasoning Comprehension Task: Identification and Reconstruction of Implicit Warrantscited by 55
  3. 2018Embedding Uncertain Knowledge Graphscited by 25
  4. 2022Rule Mining over Knowledge Graphs via Reinforcement Learningcited by 5
  5. 2024Understanding Enthymemes in Argument Maps: Bridging Argument Mining and Logic-based Argumentationcited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.