  Sequence-based neural networks show significant sensitivity to syntactic
structure, but they still perform less well on syntactic tasks than tree-based
networks. Such tree-based networks can be provided with a constituency parse, a
dependency parse, or both. We evaluate which of these two representational
schemes more effectively introduces biases for syntactic structure that
increase performance on the subject-verb agreement prediction task. We find
that a constituency-based network generalizes more robustly than a
dependency-based one, and that combining the two types of structure does not
yield further improvement. Finally, we show that the syntactic robustness of
sequential models can be substantially improved by fine-tuning on a small
amount of constructed data, suggesting that data augmentation is a viable
alternative to explicit constituency structure for imparting the syntactic
biases that sequential models are lacking.
