  Can recurrent neural nets, inspired by human sequential data processing,
learn to understand language? We construct simplified datasets reflecting core
properties of natural language as modeled in formal syntax and semantics:
recursive syntactic structure and compositionality. We find LSTM and GRU
networks to generalise to compositional interpretation well, but only in the
most favorable learning settings, with a well-paced curriculum, extensive
training data, and left-to-right (but not right-to-left) composition.
