Syntax Acquisition

Syntax acquisition in machine learning focuses on how artificial neural networks, particularly transformer models, learn to understand and generate grammatically correct sentences. Current research investigates how different training objectives and data modalities (speech vs. text, developmental vs. adult corpora) influence a model's ability to generalize syntactic structures, with a focus on identifying crucial training phases and emergent properties like syntactic attention structures. These studies are significant because they shed light on the mechanisms underlying language learning, potentially leading to improved natural language processing models and a deeper understanding of human language acquisition.