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.

研究基于序列的神经网络与基于树的网络在句法任务上的表现，并比较常见的句法表示方法对句法结构带来的影响，同时发现基于组成结构的网络更具鲁棒性，通过少量构建的数据 fine-tune 可以显着提高序列模型的性能，表明数据增强是为序列模型注入句法偏置的可行替代方案。