Compositional Split

Compositional generalization, the ability of models to handle novel combinations of previously seen components, is a crucial challenge in machine learning, particularly in natural language processing. Current research focuses on improving model performance on compositional splits of datasets—specifically designed to test this generalization ability—through methods like data augmentation (e.g., subtree substitution, iterative augmentation), structurally diverse sampling during training, and the development of novel architectures incorporating structural operations (e.g., reordering and fertility layers). Addressing this challenge is vital for building more robust and generalizable AI systems capable of handling complex, real-world tasks, impacting fields ranging from semantic parsing to question answering.