# A Sentence-Matching Model Based on Multi-Granularity Contextual Key Semantic Interaction
## 5 Conclusions  
This paper presents a sentence-matching model called the multi-granularity contextual key semantic interaction model (MCKI). The model incorporates pre-trained language model embeddings and BiGRU to capture contextual information effectively. It employs a semantic feature extraction mechanism that combines a multi-scale CNN and multi-level CNN to extract semantic contextual information at different granularities and levels. The introduction of a key semantic alignment mechanism by integrating multi-head self-attention and cross-attention mechanisms emphasizes key semantic information between sentence pairs. By combining three interaction methods, the model comprehensively considers both the similarity and difference between sentences, resulting in enhanced matching performance.  
The effectiveness of the proposed model and its components was evaluated on public datasets. The findings demonstrated that in open-domain datasets, matching key semantic elements effectively eliminates irrelevant information, leading to significant improvements, especially when sentence pairs contain high levels of noise. Moreover, the proposed methodology in this study achieved comparable performance to large-scale pre-trained models by utilizing lightweight pre-training language models, which proves effective when resources are limited. However, in closed-domain datasets where sentence pairs share the same theme, considering semantic nuances at different levels and granularities aids the model in distinguishing overlapping terminologies within the subject. Nevertheless, the effectiveness of model enhancements remains limited in such scenarios. Additionally, marginal effects are observed when applying improvements to more advanced pre-trained models. The limitations and shortcomings of the current research should be acknowledged. This study primarily focused on combining existing models without thoroughly exploring improvements to these models. Future research will aim to enhance known models in order to achieve better performance. Furthermore, there is a consideration to integrate graph neural networks, which can leverage both the key semantic information and structural aspects of sentences to optimize matching performance.  
Conceptualization, J.L. and Y.L.; methodology, J.L.; software, J.L.; validation, J.L.; formal analysis, J.L.; investigation, J.L.; resources, Y.L.; data curation, J.L.; writing--original draft preparation, J.L.; writing--review and editing, Y.L.; supervision, Y.L.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.  
The research was funded by the Key projects of science and technology plan of Yunnan Provincial Department of Science and Technology (grant number 202201 AS070029).  
Not applicable.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Multi-Scale CNN** & **Multi-Level CNN** & **Acc (\%)** \\ \hline (3, 4, 5) & (3, 4) & 83.69 \\ (3, 4, 5) & (3, 4, 5) & 83.47 \\ (3, 4) & (3, 4) & 83.54 \\ \hline (1, 2, 3) & & 83.62 \\ (2, 3, 4) & (3, 4) & 83.65 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Effect of different scale CNN combinations on model performance.