In this thesis, we try to build a connection between the two schools by
introducing syntactic inductive biases for deep learning models. We propose two
families of inductive biases, one for constituency structure and another one
for dependency structure. The constituency inductive bias encourages deep
learning models to use different units (or neurons) to separately process
long-term and short-term information. This separation provides a way for deep
learning models to build the latent hierarchical representations from
sequential inputs, that a higher-level representation is composed of and can be
decomposed into a series of lower-level representations. For example, without
knowing the ground-truth structure, our proposed model learns to process
logical expression through composing representations of variables and operators
into representations of expressions according to its syntactic structure. On
the other hand, the dependency inductive bias encourages models to find the
latent relations between entities in the input sequence. For natural language,
the latent relations are usually modeled as a directed dependency graph, where
a word has exactly one parent node and zero or several children nodes. After
applying this constraint to a Transformer-like model, we find the model is
capable of inducing directed graphs that are close to human expert annotations,
and it also outperforms the standard transformer model on different tasks. We
believe that these experimental results demonstrate an interesting alternative
for the future development of deep learning models.

我们在本文中介绍了两种归纳偏差方法，分别针对语法结构和依赖关系，实现了深度学习模型的归纳偏差，并通过这些方法建立了深度学习模型的潜在分层表示，使模型能够处理复杂的序列结构并在语言处理任务中取得了优秀的效果。