Neuro-Symbolic Approaches in NLP: Integrating Logic and Learning for Transparent Language Understanding

Abstract

Natural Language Processing (NLP) has seen transformative advancements through deep learning, yet these models often lack interpretability, logical reasoning, and robustness to novel scenarios. Neuro-symbolic approaches have emerged as a promising paradigm that combines the strengths of symbolic reasoning with the representational power of neural networks to achieve transparent and explainable language understanding. This paper explores how integrating logic and learning addresses critical issues such as data inefficiency, lack of reasoning capabilities, and the opaque nature of neural architectures. We present an in-depth analysis of various neuro-symbolic models applied to NLP tasks, discussing their design principles, benefits, and limitations. An experimental study is conducted to evaluate a hybrid framework on tasks like natural language inference and question answering, showing that neuro-symbolic systems outperform pure neural approaches in terms of both accuracy and explainability. The results highlight the potential of neuro-symbolic methods to bridge the gap between human-like reasoning and machine learning, paving the way for a new era of NLP models that are not only powerful but also inherently interpretable.