Meta Semantics: Towards better natural language understanding and reasoning

Natural language understanding is the study of making machines understand the daily used informal text. There are two main categories of methods, statistic-based methods and rule-based methods. Benefiting from the blow-up of deep learning algorithms such as transformer[1], the statistic-based methods upgrade from the traditional Bayesian methods and have better robustness. On the hand, the rule-based methods are wildly used in expert systems, which are run by handwritten rules from experts and use the patterns to map the natural language to machine-readable commands such as SQL, the LUNAR system[2], as an example, which is used in the analysis of lunar geology. Although both methods have got great achievements, there still exist some main challenges that we need to resolve. In section 2, we will discuss the success and challenges of the existing natural language understanding models. In section 3, a potential solution to the OOV problem from word embedding which limits the deep neural method to reasoning and understanding will be presented. In section 4, we will propose our semantic model in detail to move the natural language understanding into the next stage.