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Enhancing Argument Representation With AI: A Reasoning Model for Answering “Why” Questions
Abstract
Analyzing arguments in English texts when addressing “why” questions is still a challenge in textual analysis. This article presents a reasoning model designed to enhance the interpretation of argumentation by systematically addressing “why” questions. This article uses qualitative analysis and advanced computational linguistics. Key patterns and structures were identified through manual annotation and validated using natural language processing tools, including transformer models and contextualized word embeddings. Results show that the model's superior performance in decoding complex arguments and outperforming existing frameworks regarding accuracy and explainability in answering “why” questions. By generating clear responses to these kinds of questions, the model bridges gaps in text analysis, and this can offer practical applications for language teachers and learners in teaching and learning argumentation and researchers in analyzing persuasive discourse. Further research directions may promise broader impacts on automated text analysis and interdisciplinary discourse studies.
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