Argument Classification

Argument classification, a subfield of argument mining, focuses on automatically identifying and categorizing the components of arguments within text (e.g., premises, claims) and their relationships (e.g., support, attack). Current research emphasizes developing robust models, often leveraging deep learning architectures like BERT and transformer-based LLMs, to improve accuracy across diverse datasets and languages, including exploring in-context learning and fine-tuning strategies. This work has significant implications for various fields, including improving the explainability of AI systems, facilitating better understanding of complex texts, and advancing natural language processing capabilities.