## 2 Related Work  
ELIZA is the first keyword-based chatbot invented in 1966 [18]. Instead of answering questions, ELIZA acted more like a psychotherapist by questioning users based on their responses. Since then, many different types of chatbots have been created: covering closed, general, or open knowledge domains; with voice or text communication channels; providing interpersonal or intrapersonal services; selecting and prompting or generating answers; created with rule-based, retrieval-based, or ML-based methods. One big group of chatbots generate answers instead of prompting them to the user. These chatbots typically function in the machine translation manner, but instead of translating source language texts into the target language, they \"translate\" questions to related answers [19; 20; 21]. Some of these chatbots can consider the whole conversation (i.e., previous meaningful utterances) [22] and even mimic the communication style of a user when generating responses [23]. Despite all the positive things, generative chatbots are less accurate and require more training data to achieve sufficient accuracy. On the contrary, intent-detection-based (i.e., classification-based) chatbots do not require large training datasets and are more accurate compared to generative ones. Intent detectors, like all classifiers, can be trained using labeled instances in a supervised manner [24] and later be used to predict intentsfrom unseen but domain-related questions (more details about various intent detection techniques can be found in [25]). Supervised classifiers are trained with traditional SML or DL approaches. Traditional ML methods are usually applied to discrete feature representations as textual (e.g., word and character n-grams) or syntactic (e.g., part-of-speech tags) features (e.g., research with support vector machines in [26]). Nevertheless, major progress in addressing intent detection problems has been made due to DL. Effective DL-based research presented knowledge distillation and a posterior regularization method to detect a user's intent of leaving a service for another service provider (known as a churn detection problem) on an English microblog dataset [27]: the applied CNN method learns simultaneously from logic rules and supervised data (represented as random, skip-gram, CBOW, and gloVe embeddings). The churn detection problem was also successfully tackled in [28]: the CNN method, with bidirectional GRU and bilingual German and English fastText embeddings, was applied to a conversational English and German Twitter dataset. Another research direction covers topic-based intent detection problems. Comparative topic-based intent detection experiments in the English, Estonian, Latvian, Lithuanian and Russian languages, performed with two methods (i.e., the feed forward neural network and fastText embeddings with CNN), demonstrated the superiority of CNN [29]. Authors used rather small datasets (three English benchmark datasets that were also machine translated into Estonian, Latvian, Lithuanian, and Russian languages) but claimed to achieve state-of-the-art performance. Previously summarized research works focused on closed-set intent detection problems. However, there have been some attempts to detect even those intents that have no training data, such as, e.g., in [30]. Authors have tackled this problem for the English and Chinese languages by applying a two-fold architecture based on BiLSTM, with multiple self-attention heads to discriminate existing intents. However, if this cannot determine any intent, emerging intents are detected from the existing ones (by specifying or generalizing them) using the knowledge transfer method based on a similarity evaluation. Despite the fact that the majority of intent detection research relies on the assumption that any intent can be predicted solely from a user's question, some researchers have offered additional measures to help clarify the meaning of some questions in further conversation.