# Sentence Representation Method Based on Multi-Layer Semantic Network
## 6 Conclusions  
This paper analyzes the characteristics of sentence semantics firstly and then lists the key technologies of existing sentence representation methods. A semantic representation network based on a multi-attention mechanism is designed for sentence representation by summarizing the advantages and disadvantages of existing technologies. This method obtains the semantic information of the same sentence at different levels through a multiple attention mechanism, increases the semantic representation of the sentence, and at the same time, by adding the relative position mask between words, integrates the word order information of the sentence to reduce the uncertainty brought by the word order. Finally, quantitative and qualitative experiments are carried out on the text implication recognition task and emotion classification task. The experimental results show that compared with some traditional networks, the multi-layer semantic representation network designed in this paper significantly improved accuracy on both the SNLI data set and the multi-NLI data set. The model can promote the accuracy and comprehensiveness of sentence meaning representation. However, the Yelp data set's accuracy did not improve significantly, possibly because the model was not deep enough to capture deeper information for emotion analysis tasks.  
Although this paper explores the method of sentence representation and reasoning from sentence representation, which improves the reasoning accuracy to some extent, it is far from achieving the best effect. Because of this, future research work can be further studied from the following contents:  
In this paper, sentence representation uses multiple levels of semantic and word order information to represent the meaning of sentences. In future research work, the redundant relationship between multiple semantic information levels can be considered to deal. This paper, to extract the semantic information of multiple levels by joining together to form sentences embedded representation directly, does not consider semantic information redundancy between the relationship. Suppose the multilayer extraction between semantic meanings is similar, not only conducive to enhancing the meaning of the sentence comprehensiveness, accumulated in redundant information. In that case, this will also reduce the proportion of the core semantic information, resulting in decreased sentence accuracy.  
Figure 8: The learning curve of different alpha.  
**Author Contributions:** Conceptualization, W.Z. and L.Y.; formal analysis, X.L.; investigation, X.L.; writing--original draft preparation, L.Y.; writing--review and editing, L.Y.; funding acquisition, W.Z. All authors have read and agreed to the published version of the manuscript.  
This work was jointly supported by the Sichuan Science and Technology Program (2021 YEQ0003, 2019 YJ0189) and the Fundamental Research Funds for the Central Universities (ZYGX2019J059).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.