Syntactic Inductive Biases for Deep Learning Methods
This work addresses the challenge of making deep learning models more interpretable and effective for tasks requiring syntactic understanding, such as natural language processing, though it is incremental in building on existing transformer architectures.
The paper tackles the problem of integrating syntactic inductive biases into deep learning models to improve their ability to learn hierarchical and relational structures from sequential inputs, resulting in models that outperform standard transformers on various tasks and induce dependency graphs close to human annotations.
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.