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The meaning of a natural language utterance is largely determined from itssyntax and words. Additionally, there is evidence that humans process anutterance by separating knowledge about the lexicon from syntax knowledge.Theories from semantics and neuroscience claim that complete word meanings arenot encoded in the representation of syntax. In this paper, we propose neuralunits that can enforce this constraint over an LSTM encoder and decoder. Wedemonstrate that our model achieves competitive performance across a variety ofdomains including semantic parsing, syntactic parsing, and English to MandarinChinese translation. In these cases, our model outperforms the standard LSTMencoder and decoder architecture on many or all of our metrics. To demonstratethat our model achieves the desired separation between the lexicon and syntax,we analyze its weights and explore its behavior when different neural modulesare damaged. When damaged, we find that the model displays the knowledgedistortions that aphasics are evidenced to have.

Compositional Neural Machine Translation by Removing the Lexicon from Syntax