  Recurrent neural networks (RNNs) are the state of the art in sequence
modeling for natural language. However, it remains poorly understood what
grammatical characteristics of natural language they implicitly learn and
represent as a consequence of optimizing the language modeling objective. Here
we deploy the methods of controlled psycholinguistic experimentation to shed
light on to what extent RNN behavior reflects incremental syntactic state and
grammatical dependency representations known to characterize human linguistic
behavior. We broadly test two publicly available long short-term memory (LSTM)
English sequence models, and learn and test a new Japanese LSTM. We demonstrate
that these models represent and maintain incremental syntactic state, but that
they do not always generalize in the same way as humans. Furthermore, none of
our models learn the appropriate grammatical dependency configurations
licensing reflexive pronouns or negative polarity items.
