Combining Representation Learning with Logic for Language Processing

TL;DR

This paper explores integrating representation learning with formal logic to enhance natural language processing and knowledge base completion, aiming to reduce dependence on large annotated datasets and improve generalization.

Contribution

It investigates novel combinations of representation learning and logic, addressing data scarcity and boosting generalization in language processing tasks.

Findings

Combining logic with representation learning reduces training data requirements.

Improved generalization in language tasks through logical integration.

Potential for more efficient knowledge base completion.

Abstract

The current state-of-the-art in many natural language processing and automated knowledge base completion tasks is held by representation learning methods which learn distributed vector representations of symbols via gradient-based optimization. They require little or no hand-crafted features, thus avoiding the need for most preprocessing steps and task-specific assumptions. However, in many cases representation learning requires a large amount of annotated training data to generalize well to unseen data. Such labeled training data is provided by human annotators who often use formal logic as the language for specifying annotations. This thesis investigates different combinations of representation learning methods with logic for reducing the need for annotated training data, and for improving generalization.