# A Sentence-Matching Model Based on Multi-Granularity Contextual Key Semantic Interaction
## 2 Related Work  
With the advancement of deep learning techniques, attention mechanisms have been extensively explored by researchers to capture semantic interactions and determine the degree of matching. For example, [PERSON] et al. [11] presented an interactive attention network that employed a matching matrix to model the semantic exchange between source and target texts, improving the matching capability for low-frequency keywords. [PERSON] et al. [12] introduced a deep neural communication model that utilized BiLSTM and tree encoders to capture semantic and syntactic features. Through attention mechanisms, they designed deep communication modules to capture local and global interactions between semantic and syntactic features, thereby enhancing semantic comprehension. However, these studies primarily focus on modeling English sentences and mainly address single-granularity text semantic matching. They primarily analyze semantics from a single level and do not take into account the characteristics of the Chinese language. As a result, accurately measuring semantic relationships between Chinese texts is hindered. Several studies have investigated multi-granularity text semantic matching to comprehensively assess the semantic similarity and matching degree between texts. These studies have explored deep fusion techniques at the character and word levels. For example, [PERSON] et al. [13] utilized a soft alignment attention mechanism to enhance local information in sentences at different levels, capturing feature information and correlations from multiple perspectives. [PERSON] et al. [14] employed interactive information to model diverse text pairs across various tasks and languages. [PERSON] et al. [15] concurrently considered deep and shallow semantic similarity, as well as granularity at the lexical and character levels, enabling a deeper exploration of similarity information. [PERSON] et al. [16] proposed semantic similarity analysis through the fusion of words and phrases. Considering the characteristics of Chinese text, several research studies have employed character-level and word-level approaches to develop semantic matching models. For instance, [PERSON] et al. [17] proposed a deep feature fusion model that combines various deep learning structures to capture semantic information. They generate a similarity tensor using three matching strategies. [PERSON] et al. [18] designed a lattice-based approach that constructs word grids by selecting character and word paths and utilized CNN to generate sentence representations. [PERSON] et al. [19] introduced a neural graph matching network for Chinese text matching, constructing a graph that includes all possible characters and words in a sentence. They employed graph neural networks to generate graph representations for predicting the matching degree. [PERSON] et al. [20] presented a multi-granularity fusion model that combines LSTM encoding structures to extract features at different granularities. They employed an interactive matching layer to capture and fuse more interaction features. [PERSON] et al. [21] proposed a multi-granularity alignment model that utilizes attention mechanisms to capture interaction features between different granularities and merge them for obtaining matching representations. [PERSON] et al. [22] introduced a language knowledge-enhanced graph transformer to update character representations and semantic representations, obtaining the final sentence representation. Some studies attempted to model text representations using pinyin (phone transcription) and radicals. [PERSON] et al. [23] proposed a radical-guided correlation model that extracts features from characters, radicals, and character-radical associations for Chinese text classification. [PERSON] et al. [24] proposed a multi-granularity interaction model based on pinyin and radicals for Chinese semantic matching. In addition to character and word information, they further incorporated multi-granularity features of Chinese pinyin and radicals.