## 7 Conclusions  
In this research, we were solving a supervised intent detection problem for the English language and four morphologically complex languages, i.e., Estonian, Latvian, Lithuanian, and Russian. This problem has been tackled by seeking the most accurate models via automatic DNN hyperparameter optimization. In our research, two types of word embeddings (fastText, BERT), three types of DNNs (CNN, LSTM, BiLSTM), their different architectures (shallower and deeper), and various hyperparameter values (e.g., activation functions, numbers of neurons, dropouts) have been explored. The optimization was performed on three English benchmark datasets (containing 2, 5, and 8 intents) that were also manually translated into other languages.  
Despite the fact that very strict conclusions cannot be drawn due to a lack of statistical significance in the result differences, some trends are apparent: (1) DNN hyperparameter optimization is the right solution when seeking accurate models for the larger training datasets; (2) the BERT embeddings type is an especially good vectorization choice for morphologically complex languages, whereas English can benefit from fastText as well; (3) the CNN classifier allows us to reach high accuracy levels despite the dataset or language; the other classification techniques are equally good only with enough training data.  
The best accuracies were achieved on three testing datasets with 2/5/8 intents and are equal to 0.991/0.890/0.712, 0.972/0.890/0.644, 1.000/0.890/0.644, 0.981/0.872/0.712, and 0.972/0.881/0.661 for the English, Estonian, Latvian, Lithuanian, and Russian languages, respectively. Moreover, compared to the previously reported results in [29], our achieved accuracies are competitive on the **askUbuntu** dataset and better on the **chatbot** dataset.  
This research is important from the scientific perspective due to (1) the automatic hyperparameter optimization of DNN models for various intent detection problems and (2) the comparison of obtained results for different languages: i.e., for English and morphologically complex languages from the Finno-Ugric (for Estonian), Baltic (Latvian and Lithuanian), and Slavic (Russian) branches. Moreover, some solutions work across datasets and even languages. It allows us to anticipate that similar results can also be expected for other languages of the same branches.  
The research is important due to practical reasons. The optimal parameters are here revealed and, therefore, can be used to train other intent detection-based chatbots for the English, Estonian, Latvian, Lithuanian and Russian languages. However, higher accuracy can be expected only with larger and more representative training datasets.  
In future research, it would be useful to experiment with larger datasets, try other classification methods such as BERT fine-tuning, and even go beyond intent detection problems (by focusing on generative chatbots).  
Conceptualization, J.K.-D., K.B., and R.S.; methodology, J.K.-D.; software, J.K.-D. and K.B.; validation, J.K.-D.; formal analysis, J.K.-D. and R.S.; investigation, J.K.-D. and K.B.; resources, K.B.; writing--original draft preparation, J.K.-D.; writing--review and editing, J.K.-D., K.B., and R.S.; visualization, J.K.-D.; project administration, R.S. All authors have read and agreed to the published version of the manuscript.  
This research has been supported by the European Regional Development Fund within the joint project of SIA TILDE and the University of Latvia \"Multilingual Artificial Intelligence-Based Human-Computer Interaction\" No. 1.1.1.1/18/A/148.  
The authors declare no conflict of interest.