## 6 Discussion  
This research assumes that DNN hyperparameter optimization can be done without manual intervention. However, there are a few things that set this process in the right direction: the usage of the most promising word embedding types (i.e., fastText and BERT) and the most suitable classifiers (CNN, LSTM, and BiLSTM), adjusted to deal with the text. Moreover, two hyperparameter optimization algorithms have been applied: random search (rand.suggest) and TPE (tpe.suggest) that combines exploration (reaching new regions of hyperparameter values) and exploitation (searching for optimal solutions in a given region of hyperparameter values) strategies. Zooming into Tables 5-9 allows us to make the following statements. With some rare exceptions, all obtained results are reasonable because they exceed random and majority baselines (presented in Table 4). The best results on testing splits for each dataset and each language (English (EN), Estonian (ET), Latvian (LV), Lithuanian (LT), and Russian (RU)) are summarized in Figure 2. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**fastText embeddings**} \\ \hline  & \multicolumn{2}{c}{**chatbot**} & \multicolumn{8}{c}{**askUbuntu**} & \multicolumn{2}{c}{**webapps**} \\ \hline  & CNN & LSTM & BiLSTM & CNN & LSTM & BiLSTM & CNN & LSTM & BiLSTM \\ \hline tpe.suggest & 0.972 & 0.962 & 0.972 & 0.780 & 0.752 & 0.853 & 0.356 & 0.508 & 0.508 \\ \hline rand.suggest & 0.915 & 0.972 & **0.981** & 0.844 & 0.339 & 0.853 & 0.153 & 0.390 & 0.492 \\ \hline  & \multicolumn{8}{c}{**BERT embeddings**} \\ \hline  & \multicolumn{2}{c}{**chatbot**} & \multicolumn{2}{c}{**askUbuntu**} & \multicolumn{2}{c}{**webapps**} \\ \hline  & CNN & LSTM & BiLSTM & CNN & LSTM & BiLSTM & CNN & LSTM & BiLSTM \\ \hline tpe.suggest & 0.981 & 0.972 & 0.962 & 0.844 & 0.651 & 0.771 & 0.661 & 0.542 & 0.492 \\ \hline rand.suggest & 0.972 & 0.972 & 0.962 & **0.872** & 0.706 & 0.761 & **0.712** & 0.508 & 0.525 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Evaluated accuracies on the Lithuanian testing datasets with optimized models. For the other notation, see Table 5. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**fastText embeddings**} \\ \hline  & \multicolumn{2}{c}{**chatbot**} & \multicolumn{2}{c}{**ask Ubuntu**} & \multicolumn{2}{c}{**webapps**} \\ \hline  & CNN & LSTM & BiLSTM & CNN & LSTM & BiLSTM & CNN & LSTM & BiLSTM \\ \hline tpe.suggest & 0.934 & 0.943 & 0.943 & 0.771 & 0.743 & 0.798 & 0.610 & 0.542 & 0.610 \\ \hline rand.suggest & 0.830 & 0.934 & 0.962 & 0.780 & 0.752 & 0.780 & 0.271 & 0.593 & 0.627 \\ \hline  & \multicolumn{8}{c}{**BERT embeddings**} \\ \hline  & \multicolumn{2}{c}{**chatbot**} & \multicolumn{2}{c}{**ask Ubuntu**} & \multicolumn{2}{c}{**webapps**} \\ \hline  & CNN & LSTM & BiLSTM & CNN & LSTM & BiLSTM & CNN & LSTM & BiLSTM \\ \hline tpe.suggest & 0.962 & 0.962 & 0.934 & 0.844 & 0.798 & 0.826 & 0.661 & 0.593 & 0.644 \\ \hline rand.suggest & **0.972** & 0.943 & 0.953 & **0.881** & 0.761 & 0.844 & 0.576 & 0.610 & 0.576 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Evaluated accuracies on the Russian testing datasets with optimized models. For the other notation, see Table 5.  
As it can be seen from Figure 2, the best and more stable results across different languages are achieved with fewer intents but more training instances (i.e., the **chatbot** dataset), and the worse results are with more intents and less training instances (**webapps**). The validation split (which is 20% from the shuffled training dataset) of the **webapps** dataset is extremely small (i.e., only 6 instances); moreover, these randomly selected instances do not necessarily overlap for different languages. All it means is that for some languages, the validation split happened to be less representative (and less consistent with the testing dataset) than for the others.