## 3 Results and Discussion
### Fine-Tuning for Semantic Address Matching  
To explore whether there is overfitting, we tested the validation dataset by fine-tuning it while also spacing the number of iterations by 500, and determined the set of hyperparameters that worked best. In this study, 80% of the data were randomly taken as the training dataset and the remaining 20% as the validation dataset. The loss gradient curves for training and validation are shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of Address-Transformer Modules** & **Training Time** & **Training Loss** & **Mean Evaluation Loss** & **Accuracy** \\ \hline
6 & 6 h 19 m 36 s & 0.3864 & 0.3615 & 90.62\% \\
8 & 8 h 21 m 54 s & 0.3395 & 0.3258 & 90.98\% \\
10 & 10 h 23 m 5 s & 0.3016 & 0.3003 & 91.21\% \\
12 & 12 h 32 m 45 s & 0.2831 & 0.2793 & 91.47\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Values for the ASM indicator for a different number of address-transformer modules.  
Figure 4: Comparison of training loss gradient curves for the ASM with Arabic numeric code replacement and without replacement, where the curve smoothness is set to 0.7, and the opaque curve is after the smoothing setting and the translucent curve is the original loss gradient curve.  
Figure 5 shows that the training loss value of the DSAMM decreases rapidly before the steps of training iterations are 1k, indicating the model's excellent learning ability and ability to make a good assessment of whether address pairs match even before the dataset is entirely learned. This has inspired us to investigate whether better matching can be achieved even with microsupervision, that is, with a small amount of supervised training data.  
Between 2k and 6k training iteration steps, the training loss value decreases more gently and steadily, indicating that the model is still learning the task objectives for the supervised data. Between the final 6k and 8k, the training loss values have largely flattened out, demonstrating that the DSAMM instance has been sufficiently learned by that number of iterations to warrant further training. The final training loss values ranged from 0.01 to 0.05, indicating that the fine-tuning training was effective, and the exact metric values will be discussed further in the subsequent comparative experimental analysis. In addition, the trend of the loss values is like that of the training loss values, with a \"high rate of decline--slow decline--gradual levelling off\". The final loss values for the validation datasets range from 0.04 to 0.05, indicating that there is no overfitting. We selected the optimal iteration step of the validation datasets as the DSAMM instance after training 8 epochs.  
We set up comparative experiments with various gradients of the proportion of training sets to examine whether the DSAMM only needs a limited number of labelled training datasets to attain high matching precision. Figure 6 displays the matching prediction precision with 1%, 5%, 10%, 15%, and 20% of the original training datasets. When employing only 15% of the initial training set of labelled address pairings, the DSAMM achieves a matching precision of above 0.80, and 0.84 when using 20%. It demonstrates that the DSAMM can perform well in weakly supervised learning, most likely due to the transfer learning employed in our study. Our model has fully understood the address semantics using self-supervised learning in the pretraining phase, and when then fine-tuned for task-based learning using partially supervised data, it is able to combine the advantages of the two phases mentioned above, allowing the model to perform at a high level on the task with less supervised training data. This also coincides with the research that first proposed the idea of pre-training [58].  
Figure 5: Training loss-gradient curve overlaid with validation loss-gradient curve of the DSAMM, where the curve smoothness is set to 0.7, and the opaque curve is after the smoothing setting and the translucent curve is the original loss gradient curve.