Effect Graph: Effect Relation Extraction for Explanation Generation

Jonathan Kobbe, Ioana Hulpu, Heiner Stuckenschmidt

1st Workshop on Natural Language Reasoning and Structured Explanations (@ACL 2023) Long Paper

TLDR: Argumentation is an important means of communication. For describing especially arguments about consequences, the notion of effect relations has been introduced recently. We propose a method to extract effect relations from large text resources and apply it on encyclopedic and argumentative texts. B
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Abstract: Argumentation is an important means of communication. For describing especially arguments about consequences, the notion of effect relations has been introduced recently. We propose a method to extract effect relations from large text resources and apply it on encyclopedic and argumentative texts. By connecting the extracted relations, we generate a knowledge graph which we call effect graph. For evaluating the effect graph, we perform crowd and expert annotations and create a novel dataset. We demonstrate a possible use case of the effect graph by proposing a method for explaining arguments from consequences.