# A DeBERTa-Based Semantic Conversion Model for Spatiotemporal Questions in Natural Language
## 5 Experiment and Discussion
### Semantic Encoding Model Results  
This study utilizes the PyTorch deep learning framework and is programmed in Python. The integrated development environment used is JetBrains PyCharm software (version 2020.1), running on the Windows 11 64-bit operating system. After tuning the parameters of the DeBERTa-BiGRU-CRF model, the number of layers is set to 12, and the Adam optimization algorithm is employed with a learning rate of 1 \(\times\) 10\({}^{-5}\). To prevent overfitting, adam_epsilon and dropout mechanisms are applied. The model's batch size is set to 64, with the number of epochs being 60. The dataset is divided into training, validation, and test sets in an 8:1:1 ratio, with specific corpus statistics shown in Table 7.  
In the comparative experiments, the model performance was evaluated using metrics commonly employed in sequence labeling tasks, including precision, recall, and F1 score. The DeBERTa-BiGRU-CRF model demonstrated improvements in accuracy by 12.33%, 7.32%, and 2.71% over the baseline models BiLSTM-CRF, BERT-BiLSTM-CRF, BERT-BiGRU-CRF, and T5, respectively; recall increased by 12.97%, 7.52%, 3.63%, and 0.9%; and the F1 score improved by 11.93%, 7.18%, 2.53%, and 1.17%. To facilitate a more intuitive visualization of the experimental data, Figure 6 presents and compares the results of the different models using a bar chart. We performed a comparative analysis using the training process curves of the validation dataset. As indicated by the curves in Figure 7, the gradual decrease in the loss function and increase in accuracy reflect the learning capabilities of the five models. The eventual stabilization of the curves indicates that each model has developed into a stable structure with decision-making capability. In terms of training outcomes, the model proposed in this paper has the highest training accuracy, with an accuracy of 0.9562. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Corpus** & **Training Set** & **Validation Set** & **Test Set** \\ \hline Character count & 52,311 & 6234 & 6449 \\ Question count & 7508 & 948 & 960 \\ Code count & 13,618 & 1656 & 1799 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Training corpus statistics. Figure 6: Precision, recall, and F1 score of different models. Through the analysis of the experimental results, it can be seen that the BiLSTM-CRF model has limitations in its feature representation ability, especially in dealing with polysemy, long-distance dependencies, and generalization, which directly affects the accuracy of the results. The BERT model, although it successfully incorporates the contextual information, ignores the dependencies between the words, and thus, its performance is better than that of BiLSTM-CRF but still not as good as the model proposed in this paper. Similarly, although BERT considers contextual information, it does not fully consider absolute positional information, which makes its performance better than LSTM-CRF, but its F1 value is lower than the model in this paper. T5 shows a stronger ability in natural language generation compared to other large models, but its accuracy is slightly lower in the application scenarios of spatiotemporal questioning compared to the method proposed in this paper, mainly because the model in this paper has been specially designed to be more accurate in the application scenarios of spatiotemporal questioning, but its performance is still lower than the model proposed in this paper because the model in this paper has been specifically designed to better fit the processing needs of spatiotemporal interrogative sentences, while T5 is more adept at a wide range of natural language processing tasks due to its wide applicability, which requires a lot of pre-training and fine-tuning processes. The DeBERTa model adopted in this paper, with its decoupled attention mechanism and introduced absolute positional encoding, achieves a significant performance improvement, not only optimizing the pre-training efficiency but also substantially enhancing the generalization ability.