Titlebook: Robust Argumentation Machines; First International Philipp Cimiano,Anette Frank,Benno Stein Conference proceedings‘‘‘‘‘‘‘‘ 2024 The Editor

宿醉

蒸發(fā)

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圓柱

PolArg: Unsupervised Polarity Prediction of?Arguments in?Real-Time Online Conversationsodels instead. Our experimental evaluation found this approach to be more effective for X conversations than a model fine-tuned on Kialo debates, but less effective for HN posts (which are more technical and less argumentative). Finally, we provide an open-source application for converting discussions on these platforms into argument graphs.

手段

Enhancing Abstract Argumentation Solvers with?Machine Learning-Guided Heuristics: A Feasibility Stud search-based solver. Additionally, we propose a strategy for handling varying prediction qualities. Our approach significantly reduces both the number of backtracking steps and the overall runtime, compared to standard existing heuristics for search-based solvers, while still providing a correct solution.

Peak-Bone-Mass

Cluster-Specific Rule Mining for?Argumentation-Based Classificationtermined by formulating arguments based on the respective cluster-specific rules for the different classes. Finally, the justification status of the arguments is determined using the argumentative inference method of the structured argumentation approach.