论文标题
语法的表示[掩码]有用：递归LSTMS组成和依赖性结构的影响
Representations of Syntax [MASK] Useful: Effects of Constituency and Dependency Structure in Recursive LSTMs
论文作者
论文摘要
基于序列的神经网络对句法结构显示出显着的敏感性，但是与基于树的网络相比，它们在句法任务上的表现仍然不佳。 Such tree-based networks can be provided with a constituency parse, a dependency parse, or both.我们评估这两种代表性方案中的哪一个更有效地引入了句法结构的偏见，从而提高了主题 - 动词一致性预测任务的性能。我们发现，基于选区的网络比基于依赖关系的网络更强大，并且结合两种类型的结构不会产生进一步的改进。最后，我们表明，通过对少量构造的数据进行微调，可以通过微调来大大改善顺序模型的句法鲁棒性，这表明数据增强是赋予句法偏见的可行替代方案，即缺乏顺序模型。
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.
