Syntactic Inductive Biases for Deep Learning Methods

TL;DR

This thesis introduces syntactic inductive biases for deep learning, enabling models to better capture hierarchical and relational structures in data, improving interpretability and performance.

Contribution

It proposes two novel inductive biases for deep learning models—one for constituency and one for dependency structures—enhancing their ability to learn hierarchical and relational representations.

Findings

Models learn to process logical expressions based on syntactic structure.

Dependency bias enables models to induce graphs close to human annotations.

Biases improve performance on various natural language tasks.

Abstract

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.