## 2 Related Work  
In this review, we exclude all outdated rule-based or keyword-based techniques used in chatbot technology, focusing on the machine learning (ML) approaches [2] only, with particular attention paid to DL solutions (a review on different types of chatbots can also be found in Reference [3]). Existing ML-based chatbots can be grouped according to their model types, i.e., intent-detection or generative. An intent-detection model is a typical example of a text classification task [4], where a classifier learns how to predict intents (classes) from a set of requests/questions (text instances) incoming from the user (a review of different intent detection techniques can also be found in Reference [5]). Since the intent is usually selected from a closed set of candidates and the related answer is prompted to the user, there is no flexibility in the content and the wording of these answers. Traditional ML approaches typically rely on a set of textual feature types and discrete feature representations. The research in Reference [6] described the investigation of \(n\)-grams, parts of speech, and support vector machine (SVM) classifiers with three categories (expressing user intent to purchase/quit, recommend/warn, or praise/complain) on the English dataset. In general, intent detection is a broad task that can be defined as a churn detection (when a user expresses their intent to leave a service usually due to another customer) problem, a question topic identification problem, etc. The authors in Reference [7] offered an effective knowledge distillation and posterior regularization method for churn detection. Their approach enabled a convolutional neural network (CNN) applied on top of four types of pre-trained word embeddings (random, skip-gram, continuous bag-of-words, and gloVe) to learn simultaneously from three logic rules and supervised microblog English data. The research in Reference [8] also tackled the churn detection problem; however, in this case, it was solved for multilingual English and German conversations. The offered solution employed a CNN method with a bidirectional gated recurrent unit (BiGRU) applied on the pre-trained English and German fastText embeddings. The authors experimentally proved that their churn detection method tested on Twitter conversations was accurate and benefited from the multilingual approach. The authors in Reference [9] reported question topic intent-detection results for five languages (English and morphologically complex Estonian, Latvian, Lithuanian, and Russian). They investigated two neural classifiers: feed forward neural network (FFNN) and CNN with fastText embeddings. The accuracy of these classifiers was tested on three benchmark datasets (the datasets were originally in English, but the authors machine-translated them into other languages as well): askUbuntu (with 53 and 109 questions for testing and training, respectively); chatbot (with 100 and 106); webApps (30 and 59). The authors claimed that, despite extremely small training data, their system demonstrated state-of-the-art performance. In addition to previously summarized closed-set intent detection problems, some researchers dealt with a so-called zero-shot intent-detection problem by attempting to detect even those intents for which no labeled data are currently available. The research in Reference [10] presented a two-fold capsule-based architecture which (1) discriminates the existing intents with the bidirectional long short-term memory (BiLSTM) network and multiple self-attention heads, and (2) learns to detect emerging intents from the existing ones on the zero-shot via knowledge transfer (based on the evaluated similarity). The authors achieved sufficient results on two benchmark datasets: one containing English and another containing Chinese conversations. Sometimes, the correct intent cannot be determined from one incoming question and must be clarified in further conversation.