# A Sentence-Matching Model Based on Multi-Granularity Contextual Key Semantic Interaction
## 4 Experimental Results and Analysis
### Performance Comparison  
Table 2 presents the accuracy results of various models on the test sets of LCQMC and BQ datasets. Overall, pre-trained language model-based methods outperformed the other two approaches. The performance improvement of these models was more pronounced on \begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **LCQMC** & **BQ** \\ \hline Text-CNN [28; 29] & 72.80 & 68.52 \\ BiLSTM [28; 29] & 76.10 & 73.51 \\ BiMPM [28; 29] & 83.40 & 81.85 \\ DRCN [33] & 85.90 & 83.30 \\ MIPR [24] & 85.29 & 84.45 \\ MKPM [10] & 86.71 & 84.11 \\ BERT-base & 86.75 & 84.67 \\ SBERT & 84.44 & 83.64 \\ MAGE & 87.58 & 84.84 \\ MacBERT-base [34] & 87.00 & 85.20 \\ MacBERT-large [34] & 87.60 & 85.60 \\ Ernie 3.0-nano & 86.12 & 83.02 \\ Ernie 3.0-base & 87.64 & 85.83 \\
**MCKI** (BERT-base) & 87.90 & 85.28 \\
**MCKI** (Ernie 3.0-nano) & 87.65 & 83.69 \\
**MCKI** (Ernie 3.0-base) & **89.08** & **86.08** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Accuracy of different models on LCQMC and BQ. open-domain datasets compared to closed-domain datasets. This can be attributed to the emphasis on capturing essential semantic information, which allows for the elimination of irrelevant semantic details. As a result, the models demonstrated significant enhancements when handling sentence pairs with more noise. However, it is worth noting that the BQ dataset consisted of samples with a common theme, resulting in higher similarity between positive and negative instances. This increased similarity posed a challenge in distinguishing between them. Among the neural network-based methods, BiLSTM demonstrated a substantial accuracy improvement compared to Text-CNN. This is attributed to the fact that text data can be viewed as sequential data, and the recurrent structure of BiLSTM is better suited for modeling sequence features. Unlike Text-CNN, which extracts diverse text features through convolutions of various sizes, BiLSTM takes into account the dependencies between contextual information present in the text. To further enhance accuracy, BiMPM, which builds upon BiLSTM, introduces bidirectional different-perspective matching to capture contextual features and aggregates matching information using BiLSTM. Methods incorporating attention mechanisms, which enable the model to concentrate on crucial interaction information between sentences, also exhibit enhanced performance. DRCN introduced an attention mechanism in recurrent neural networks. MKPM employed bidirectional attention to select crucial words for keyword matching in sentence pairs, leading to significant improvements on the LCQMC dataset. This highlighted the importance of key semantics in sentence matching within an open-domain context. MIPR extracted semantic interaction features within and between sentences at four different granularities, resulting in notable improvements on the BQ dataset. This showcases the influence of multi-granularity semantics in sentence matching within a closed-domain setting. Pre-trained language models demonstrate exceptional performance in language modeling by acquiring contextual representations from extensive data. BERT achieved an accuracy of 86.75% on the LCQMC dataset and 84.67% on the BQ dataset. In contrast, SBERT, which employs a dual-encoder architecture to model sentence representations but lacks explicit interaction information between sentences, exhibited lower effectiveness on both datasets when compared to the cross-encoder architecture of BERT. While attention mechanisms enable models to concentrate on important words or phrases in text, they can pose challenges in distinguishing between positive and negative examples in texts with similar themes. MAGE effectively handles this challenge through the use of a context-aware interaction module and a multi-perspective fusion module. Additionally, pre-trained language models such as MacBERT and Ernie 3.0, which have undergone extensive training on large-scale Chinese data, enhance performance by improving the training process.