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.
翻译：自然语言理解是人工智能中最具挑战性的领域之一。深度神经网络方法，尤其是大型语言模块（LLM）方法，如ChatGPT和GPT-3，具有强大的灵活性，可以适应非正式文本，但在逻辑推断方面较弱，并且遭受术语外问题（OOV问题）。另一方面，基于规则的方法，如Mathematica，语义web和Lean，在推理方面表现出色，但无法处理复杂和变化的非正式文本。受语用学和结构主义的启发，我们提出了两种策略来解决OOV问题，并提出了更好的自然语言理解和推理的语义模型。
相关内容
微信扫码咨询专知VIP会员