# Deep Contrast Learning Approach for Address Semantic Matching
## 4 Discussion  
Further detailed analysis, in terms of the convergence speed during the training process, FastText has a relatively simple and clear structure, so the convergence is more effective and faster than other models. Due to the fact that TextRCNN uses bidirectional RNN structure to obtain context information, it has certain advantages over FastText on the overall information acquisition, so the result shows TextRCNN has obvious performance strengths compare to FastText. This result presents two conclusions. First of all, the semantic extraction approach that using single sentence has certain advantages over using whole sentence pair to obtain context information as input of the network. Although bidirectional RNN can make up for the deficiencies on calculating distribution of the important words, it will ignore the comparative information from the sentence pair, so it is inferior to the ABLC model in performance. Secondly, the effect difference between the TextRCNN and FastText is not very large, indicating that the additional position-wise information introduced by attention mechanism has a certain improvement, but the effect is relatively limited. This conclusion can be explained as that the address is a special text information based on certain rules. Additionally, then the distribution of semantic information related to its position is often more fixed. Therefore, even though the position information is referred, the semantic gap between addresses is small, and the overall performance improvement of model is not very obvious.  
Compared with ABLC model, ABLC-XLNet does not give an effective promotion, and has a certain amount of decline, as shown in Figure 8. As a possible explanation to this, address is a special branch of Chinese string, which contains proper nouns that refer to different places. XLNet has the ability to improve the performance on many downstream tasks, but for Chinese address descriptions, the model may lack of responsiveness to proper nouns of place. Because of this, the embedding performance cannot get a significant enhancement, and the prediction performance metrics of the model have a certain range of oscillations [44,45].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model Name** & **F1 Score** & **Accuracy** & **Recall** & **Precision** \\ \hline ABLC & 0.9504 & 0.9563 & 0.9460 & 0.9552 \\ ESIM & 0.8992 & 0.9146 & 0.9051 & 0.9020 \\ SVM & 0.7267 & 0.7782 & 0.7125 & 0.7662 \\ FastText & 0.6763 & 0.812 & 0.6132 & 0.7569 \\ TextRCNN & 0.8062 & 0.8774 & 0.7733 & 0.8424 \\ ABLC-1(XInet) & 0.8142 & 0.7515 & 0.8348 & 0.7947 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Experiment results on comparison experiment.  
Except the ABLC model proposed in this paper, the ESIM model has the best overall performance because ESIM uses local inference and inference synthesis techniques to achieve information extraction and can capture both local and global features of information. However, compared with the ABLC model proposed in this paper, its performance is slightly weaker, probably because the ABLC contrast learning model gives higher weight to the key information in addition to acquiring local and global features of information, and is good at capturing the similarities and differences between inputs via using contrast learning algorithm, thus having better classification capabilities.  
A special case shown in the Table 8, even though this paper uses the semantic similarity to do the task of matching address, the ABLC model determines that the relationship between address 1 and address 2 is \"not similar\", whereas, the subject in address 1 and the subject in address 2 refer to the same building. One possible explanation is the training set does not contain such related information, or the lack of relevant external knowledge to supplement, so that the model cannot find out some subjects with related relationships.