Argument Quality
Argument quality assessment focuses on understanding and evaluating the persuasiveness and validity of arguments, aiming to improve reasoning and decision-making across various domains. Current research emphasizes computational approaches, leveraging large language models (LLMs) and techniques like prompt tuning, counterfactual explanations, and ensemble methods to analyze argument structure, strength, and impact, often within frameworks of quantitative argumentation. This work has significant implications for applications such as automated debate systems, legal mediation, and education, offering the potential to enhance critical thinking and mitigate the spread of misinformation.
Papers
Adam-Smith at SemEval-2023 Task 4: Discovering Human Values in Arguments with Ensembles of Transformer-based Models
Daniel Schroter, Daryna Dementieva, Georg Groh
Similarity-weighted Construction of Contextualized Commonsense Knowledge Graphs for Knowledge-intense Argumentation Tasks
Moritz Plenz, Juri Opitz, Philipp Heinisch, Philipp Cimiano, Anette Frank