Meta Semantics: Towards better natural language understanding and reasoning
This work addresses the problem of improving natural language understanding and reasoning for AI systems, potentially advancing capabilities in areas like chatbots and automated reasoning, but appears incremental as it builds on existing methods.
The paper tackles the challenge of natural language understanding by addressing the out-of-vocabulary problem and proposing a semantic model to improve reasoning, aiming to enhance performance in logical deduction and handling informal text.
Natural language understanding is one of the most challenging topics in artificial intelligence. Deep neural network methods, particularly large language module (LLM) methods such as ChatGPT and GPT-3, have powerful flexibility to adopt informal text but are weak on logical deduction and suffer from the out-of-vocabulary (OOV) problem. On the other hand, rule-based methods such as Mathematica, Semantic web, and Lean, are excellent in reasoning but cannot handle the complex and changeable informal text. Inspired by pragmatics and structuralism, we propose two strategies to solve the OOV problem and a semantic model for better natural language understanding and reasoning.