## 1 Introduction  
Our society is not imaginable without virtual assistants and chatbots such as Siri, Alexa, and Cortana. The AI technology in chatbots is responsible for intelligent human-computer interaction [1]; chatbots can answer vital questions 24/7 [2; 3] and even assist in learning [4]. Usually, chatbots are composed of the following components: natural language understanding (NLU; responsible for comprehension of user's questions), dialog management (responsible for a fluent conversation), content (responsible for chatbot's properly selected answers), and custom data (that helps to personalize conversations). The focus of this research is on the NLU module (specifically, on the intent detection) because comprehension of the structure and meaning of user questions is the core of smooth operation in any dialog system. The intent detection task that is a typical example of text classification can be solved with the rule-based or machine learning (ML) approaches. However, the creation of rules usually requires a lot of human labor; rules bind to the domain and scope of specific dialog systems [5; 6; 7] and, therefore, are hardly transferable to new systems. On the contrary to rule-based approaches, supervised ML (SML) techniques (especially the deep neural network (DNN)-based techniques) can be chosen instead. In the case of SML, a model (equivalent to a set of rules) is learned automatically from question-answer pairs as training instances [8; 9]. Afterward, the learned model can be used to predict intents from the unseen user's questions. The intent detection task can be complicated due to a number of reasons. Language-independent problems arise due to small training datasets (where intents are covered by only a few questions) and too-short questions (carrying too little information to describe some intent clearly), whereas language-dependent reasons (as free word-order in a sentence, inflection of words, grammatical errors, jargon, abbreviations, or missing diacritics) are more complicated and more difficult to tackle. In recent years, traditional SML approaches have almost been completely replaced with deep learning (DL). Moreover, neural vectorization has helped to overcome many language-dependent problems as well. Tremendous progress in this area has opened opportunities to apply DNNs for various tasks: image recognition, price prediction, and processing of natural language. In the field of NLP, DNNs are used for language generation, machine translation, text classification, named entity recognition, and many other tasks. Moreover, the development of dialogue systems is not an exception: DNN-based ML methods are used for end-user dialog systems [10; 11; 12; 13] and their separate components [14; 15; 16; 17]. DNN methods represent an enormous group of methods, having in mind their different types, architectures, and hyperparameter values. Therefore, a manual search for optimal DNN-based models can be very time consuming; moreover, some parameter values simply cannot be investigated as they are considered to be less effective in the English language. Due to these reasons, we assume that automatic DNN parameter optimization is the right solution if seeking the most accurate models. Hence, in this research, parameters of three DNN classifiers (convolutional neural network (CNN); long short-term memory method (LSTM), and bidirectional LSTM (BiLSTM)), with two types of word embeddings (fastText and BERT) on three different datasets (containing 2, 5, and 8 intents) for five different languages (English (EN), Estonian (ET), Latvian (LT), Lithuanian (LV), and Russian (RU)) were tuned automatically.