# Sentence Representation Method Based on Multi-Layer Semantic Network
## 1 Introduction  
Natural language inference (NLI) has become one of the most important benchmark tasks in the field of natural language understanding because of its complex language understanding and in-depth information involved in reasoning [1]. Natural language reasoning technology is widely used in automatic reasoning [2], machine translation [3], question answering systems [4], and large-scale content analysis. Compared with computer vision and speech recognition technology, natural language reasoning has not reached a high level because of its technical difficulties and complex application scenarios [5, 6]. Once the natural language reasoning technology makes a breakthrough and realizes the real barrier-free communication between humans and machines, human life quality will be greatly improved. The sentence representation method's research focuses on improving the sentence representation module's performance to obtain complete and accurate semantic information coding to improve the method's performance. The sentence representation module aims to map natural language sentences into a dense vector space under the premise of keeping the sentence expression semantics unchanged. It transforms the complex logical reasoning process into the solution process of similarity between sentences and solving the relationship between sentences by computer [7, 8, 9]. Before the emergence of sentence-level representation technology, sentence semantic representation used Continuous Bag of Words (CBOW) embedded distributed representation technology based on word coding to represent the text as a fixed-length sentence vector. Although this method can calculate sentence representation succinctly and efficiently, it loses the sequence information between words. Different sentences will get the same representation regardless of the word order if they use the same word. Even if we consider the word order in a short time (the predicted context range is greater than 1), sentence representation is also affected by data sparsity and high dimensions. For example, words such as \"Paris\", \"France\", and \"capital\" are equidistant in embedded representation, but \"Paris\" is closer to \"Capital\" in semantics. [PERSON] [10] proposed an unsupervised method for characterizing the sentence paragraph vector learning continuous distributed variable text vector representation. By joining together the sentence embedded vector and other embedded vector of each word to predict the probable next word, the variable text can be phrases, sentences, or document. [PERSON] [11] extracts from specific tasks and expands the distributed semantic assumption of words in the skip-gram model [10; 12]. It is assumed that sentences with similar context information usually have the same or similar semantic information. Based on this assumption, a general sentence representation model, skip-thought, for learning high-quality sentence vectors is proposed. Unlike paragraph vector, the skip-thought model uses words to predict their context. It encodes a complete sentence to predict the sentences around it, thus obtaining sentence representation. The skip-thought model requires that the training set's text be sequential, meaning that the three sentences input must be sequential. It presents a problem for migration to social media or artificial languages generated by symbolic knowledge. To solve this problem, [PERSON] [13] proposed Sequential. Denoising Auto-Encoder (SDAE). SDAE uses the deep network model built layer by layer with an automatic noise reduction encoder to automatically learn useful semantic features from many unsupervised data. It then uses the extracted semantic features to predict source sentences. The biggest difference from skip-thought is that SDAE can train sentence sets in any order. Based on the distribution hypothesis at the sentence level [14], [PERSON] [13] proposed another sentence representation method FastSent.