  Compositional generalization is the ability to generalize systematically to a
new data distribution by combining known components. Although humans seem to
have a great ability to generalize compositionally, state-of-the-art neural
models struggle to do so. In this work, we study compositional generalization
in classification tasks and present two main contributions. First, we study
ways to convert a natural language sequence-to-sequence dataset to a
classification dataset that also requires compositional generalization. Second,
we show that providing structural hints (specifically, providing parse trees
and entity links as attention masks for a Transformer model) helps
compositional generalization.
