# A Sentence-Matching Model Based on Multi-Granularity Contextual Key Semantic Interaction
## 4 Experimental Results and Analysis
### Baseline Models  
In this study, representative methods from three categories, namely neural network-based methods, attention-based methods, and pre-trained language model-based methods, were selected as comparative models. The chosen models are as follows:  
* Neural Network-based Methods: Text-CNN [30]: a convolutional neural network model commonly used for sentence classification and matching tasks. BiLSTM [31]: a variant of recurrent neural networks that processes sentences using LSTM units, capturing long-term and short-term dependencies from both forward and backward contexts. BiMPM [32]: A sentence-matching model based on BiLSTM. It utilizes BiLSTM to represent contextual information and performs matching from two directions and four different perspectives. The matching results are then aggregated using BiLSTM.
* Attention-based Methods:DRCN [33]: a densely connected co-attention recurrent neural network where each layer utilizes attention features and hidden features from all preceding recurrent layers.  
MIPR: A multi-granularity interaction model based on pinyin and radicals for Chinese semantic matching. The interaction features between different granularities and sentences are aggregated to generate the final matching representation and predict the matching degree.  
MKPM: A sentence-matching method based on multi-keyword pair matching. It employs attention mechanisms for sentence pairs and selects the most important keyword pairs from both sentences to represent their semantic relationships. This approach helps to avoid interference from redundancy and noise.  
* Pre-trained Language Model-based Methods:  
BERT: A renowned pre-trained language model. This study utilizes BERT-base as the encoder to generate representations for sentence pairs. The CLS vector output is fed into a linear layer to predict the matching relationship between the pairs.  
SBERT: A semantic textual similarity method based on BERT. It employs a Siamese and triplet network structure to obtain semantically meaningful sentence representations.  
MAGE: A method for multi-scale context-aware interaction in sentence pair matching, based on multi-granularity embeddings. It aligns semantic relationships using context-aware interaction modules and combines local semantic features with attention representations using a multi-perspective fusion approach. This study utilizes BERT-base as the encoder and predicts the matching relationship between sentence pairs using a linear layer.  
MacBERT [34]: An enhanced Chinese pre-trained language model based on BERT. It replaces the MLM (Masked Language Model) task in BERT with the Mac (MLM as correction) task, which helps alleviate the discrepancy between pre-training and fine-tuning stages.  
ERNIE 3.0 [35]: A large-scale knowledge-enhanced pre-trained language model that combines autoregressive and autoencoding networks. This study conducts experiments using both ERNIE 3.0-base and ERNIE 3.0-nano as encoders. Similar to BERT, the CLS vector output is fed into a linear layer to predict the matching relationship between sentence pairs.