  We present a dataset for evaluating the grammaticality of the predictions of
a language model. We automatically construct a large number of minimally
different pairs of English sentences, each consisting of a grammatical and an
ungrammatical sentence. The sentence pairs represent different variations of
structure-sensitive phenomena: subject-verb agreement, reflexive anaphora and
negative polarity items. We expect a language model to assign a higher
probability to the grammatical sentence than the ungrammatical one. In an
experiment using this data set, an LSTM language model performed poorly on many
of the constructions. Multi-task training with a syntactic objective (CCG
supertagging) improved the LSTM's accuracy, but a large gap remained between
its performance and the accuracy of human participants recruited online. This
suggests that there is considerable room for improvement over LSTMs in
capturing syntax in a language model.
