Argument Structure

Argument structure extraction (ASE) focuses on automatically identifying and representing the relationships between arguments within text, aiming to improve document understanding and analysis across various domains. Current research emphasizes developing robust models, often employing transformer-based architectures and convolutional neural networks, that effectively leverage contextual information to overcome challenges posed by unstructured discourse. These advancements are driven by a need for improved efficiency in data annotation, often addressed through transfer and active learning techniques, and a focus on creating models resilient to variations in data and input formats. The resulting improvements in ASE have significant implications for fields requiring automated text analysis, such as legal, medical, and scientific literature processing.