# A Deep Fusion Matching Network Semantic Reasoning Model
## 1 Introduction  
Natural language inference (NLI) is a process in which the abstract representation of natural language text pairs becomes space vectors. The reasoning learns the potential relationship between text pairs. NLI has become one of the most critical benchmark tasks in natural language understanding because of its complex language understanding and in-depth information involved in reasoning. At present, NLI technology mainly consists of three parts: encoding, sentence representation (understanding), and reasoning learning. Among them, sentence reasoning learning is still a long way from the goal of practical application--the construction and optimization of the reasoning model. The research on deep learning for the NLI model is still in its infancy. Although the existing deep learning algorithm models, such as cyclic neural network and convolutional neural network, have achieved initial results in the construction of the reasoning model, they have failed to achieve a breakthrough. Therefore, there is a broad space for research on the construction and optimization of the NLI model. The construction and optimization of sentence semantic representation and reasoning models have become two core problems in NLI. No matter which aspect is improved, the effect of the whole NLI method will be affected. At the same time, it is of great significance to study the influence of the two methods on the NLI method. In existing studies, to focus on the construction and optimization of a semantic representation and inference model, input is usually simplified as sentence pairs to avoid the interference caused by miscellaneous data. Therefore, NLI technology is also known as sentence representation reasoning technology. Before the appearance of sentence-level representation technology, the method of sentence-level representation was to use CBOW embedded distributed representation technology based on word encoding to represent text as a fixed-length sentence vector. However, with the development of neural networks and deep learning, recently, sentence representation technology has gradually developed from a combination of simple word-embedded models to more complex architecture, such as the convolutional network [1], cyclic neural network [2; 3], and its deformation [4]. They have been applied to improve the performance of sentence representation. Inspired by these works, this paper decided to use a tree convolutional network for the extraction of sentence structural information. Besides sentence representation, semantic reasoning is a process that infers the logical relationship of text pairs by analyzing the internal relationship between text information and text according to a given natural language text pair. NLI mainly adopted the method based on logical formal reasoning [5; 6], which transformed sentences expressed in natural language form into logical expressions that computers can understand, and then it realized semantic reasoning using a logic interpreter. [PERSON] [7] proposed a logic-based reasoning method COGEX, which is based on logic, to represent the relationship between inferential text pairs, such as syntactic objects, syntactic subjects, and causal relationships. In addition to the logical representation of input text pairs, the method also uses knowledge base content with logical representation. [PERSON] [8] proposed a dependency syntactic logic reasoning method, which parses the syntactic relationship of the text, constructs a syntax tree, and then completes semantic reasoning through the relationship between the parent node and the child node. Logic-based reasoning technology has achieved good results in processing small-scale data. However, with the increasing data volume and the sentence structure complexity, the applicability and accuracy of the model are limited [9]. With the development of deep learning in natural language processing, semantic reasoning technology gradually changes from logic-based reasoning technology to deep learning-based reasoning technology [10].