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Standard methods in deep learning for natural language processing fail tocapture the compositional structure of human language that allows forsystematic generalization outside of the training distribution. However, humanlearners readily generalize in this way, e.g. by applying known grammaticalrules to novel words. Inspired by work in neuroscience suggesting separatebrain systems for syntactic and semantic processing, we implement amodification to standard approaches in neural machine translation, imposing ananalogous separation. The novel model, which we call Syntactic Attention,substantially outperforms standard methods in deep learning on the SCANdataset, a compositional generalization task, without any hand-engineeredfeatures or additional supervision. Our work suggests that separating syntacticfrom semantic learning may be a useful heuristic for capturing compositionalstructure.

Compositional generalization in a deep seq2seq model by separating syntax and semantics