## 7 Results and Discussion
### Comparison with the Baselines Using Pre-Trained Embeddings  
To analyze the impact of using pre-trained word vectors, experiments using multiple word vector models as the input embedding layers for the proposed U-IntentCapsNet model were performed. The Baselines IX-XI were trained using the context free Word2 Vec embeddings. These baselines used 100, 200 and 300 dimensional W2V embeddings. An experiments was also conducted using a pre-trained mBert as the input word vector representation for the proposed U-IntentCapsNet model. The results of these models in terms of their accuracy, precision recall and F1 scores are given in Table 11.  
The results presented in Table 11 show that the proposed model performed the best when the model utilized the vector encodings of tokens and learned the embeddings without pre-trained word representations. When analyzing the experimental results utilizing the W2V embeddings, it is worth noting that the intent detection with the W2V-100 gave a 0.8136 F1 score. A significant improvement in the results was observed in the W2V-200 (Baseline X) where the F1 increased from 0.8136 to 0.8535. By further using the W2V-300 (Baseline XI), the results were improved to a 0.8583 F1 score. Using pre-trained mBert embeddings in Baseline XII, the over-all accuracy attained by the model was 83.94% and the F1 score was 0.8391. These results show that the W2V-based baselines performed better than the contextualized embeddings. Figure 5 presents the learning curve of the W2V Baselines IX-XI and the proposed model in terms of the accuracy over the epochs. It is evident from Figure 5 that the proposed U-IntentCapsNet model attained an improved accuracy of 91.12%, surpassing the W2V-based baselines by a significant margin in fewer epochs than the baselines that might have taken more epochs to converge to their local maxima. In the context of skewed datasets such as the Urdu web queries dataset used in these experiments, where the INFO class is in the majority, the F1 scores of all the intent classes need to be analyzed to measure the quality of the trained model. Table 11 highlights the F1 score of the INFO, TRAN and NAV classes in the dataset for the W2V-based baselines and the proposed model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Baseline** & **Model** & **Prec.** & **Recall** & **F1** & **Acc.** \\ \hline IX & W2V-100 + U-IntentCapsNet & 0.8110 & 0.8242 & 0.8136 & 0.8242 \\ X & W2V-200 + U-IntentCapsNet & 0.8524 & 0.8561 & 0.8533 & 0.8561 \\ XI & W2V-300 + U-IntentCapsNet & 0.8566 & 0.8613 & 0.8583 & 0.8613 \\ XII & mBERT + U-IntentCapsNet & 0.8432 & 0.8394 & 0.8391 & 0.8394 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Intent detection results for baselines with pre-trained word representations and the proposed U-IntentCapsNet model with an embedding layer trained from scratch. Figure 5: Learning curves of baseline models with pre-trained W2V embeddings and the proposed U-IntentCapsNet model in terms of accuracy. It can be further elaborated from Table 12 that the proposed model had the highest F1 score for all the three intents. The F1 score for the INFO intent was the highest, i.e., 0.9439, among the other baselines. Baseline IX performed poorly in predicting TRAN queries with lowest F1 score of 0.2947 among all the baselines. Baseline XI underperformed the most in predicting NAV queries with an F1 score of 0.0985 among all the other baselines and the proposed model. The prediction of the proposed model for the TRAN and NAV were 0.7553 and 0.7555, respectively, which was quite satisfactory, highlighting the fact that the model was able to learn a fine-grained difference among the queries. We delved deeper into this investigation and analyzed the vector representations generated by the W2V model to understand the low performance of the model with pre-trained embedding. Five frequently-searched, two-term queries from the queries dataset given in Table 13 were analyzed.