## 2 Related Work
The authors addressed the problem of long-term dialogues when some generated utterance influences the future outcomes. They integrated encoder-decoder (generating responses) and reinforcement learning (optimizing a future reward by capturing global properties of a good conversation) paradigms. The reward was determined with ease of answering, information flow, and semantic coherence conversational properties. The offered method was trained on an English dataset containing ~0.8 million sequences and, afterward, the conversation was successfully simulated between two virtual agents. The comparative experiments with an encoder-decoder architecture demonstrated its superiority over SMT approaches and even information retrieval (IR)-based chatbots. The recurrent neural network (RNN) encoder-decoder trained with ~4.4 million Chinese microblogging post-response pairs outperformed SMT approaches [20]. The BLUE scores with the encoder-decoder (containing two LSTM neural networks with one-hot encoding for the input and the output) trained on ~0.7 million of English conversations were significantly better compared to the results with the IR method in Reference [21]. Moreover, the seq2 seq-based generative conversational model (adapted from Reference [13] with tf-idf features) can enhance the results of IR systems [22]; it was experimentally proven with a model trained on ~0.66 million QA pairs in English, but sometimes mixed with Hindi. The hybrid approach in Reference [23] benefited from the combination of both IR and generative chatbot technology; it outperformed IR and generative chatbots used alone. The method uses IR to extract QA pair candidates and then re-ranks candidates based on the attentive seq2 seq model. If some candidate is scored higher than the determined threshold, it is considered as the answer; otherwise, the answer is generated with the generation-based model. This chatbot trained on ~9 million QA pairs from an online customer service center was mainly adjusted for conversations in Chinese. The analysis reveals that chatbot research mostly focused on English and Chinese languages, which have enough resources. Despite this, chatbots for other languages were sometimes created using artificial data as in Reference [9], where Estonian, Latvian, Lithuanian, and Russian intent-detection-based chatbots were trained on machine-translated English benchmark datasets. The existing conversational generative chatbots can be used with a wide range of languages, as demonstrated in Reference [24]. However, chatbot technology was not presented for any of these languages; instead, chatbot technology was demonstrated for English, testing the power of Google machine translation tools. Intent-detection models can be accurate even when trained on the smaller datasets, whereas, for generative models, hundreds of thousands or even millions of QA pairs are usually used. The main contribution of our research is that we create a closed-domain generative chatbot by training it on an extremely small, but real dataset. Moreover, we train it on two languages (English and morphologically complex Lithuanian) and perform compare analysis. Furthermore, we investigate several encoder-decoder architectures applied to different word embedding types (one-hot encoding, fastText, and BERT). We anticipate that our findings could be interesting for researchers training generative chatbots on small data. To our knowledge, this is the first paper reporting generative chatbot results for a morphologically complex language.