## 2 Related Work
This type of intent detection is a so-called multi-turn response selection problem, which was addressed in Reference [11]. The offered deep attention matching network (DAMN) method uses representations of text segments at different granularities with stacked self-attention and then extracts truly matched segment pairs with attention across the whole context and the author's response. The authors proved the effectiveness of their method on the Ubuntu Corpus V1 (regarding the Ubuntu system troubleshooting in English) and the Douban Conversation Corpus (from social networking on open-domain topics in Chinese), both having ~0.5 million multi-turn contexts for training. Furthermore, we focus on another large group of chatbots--in particular, generative chatbots--typically functioning in the machine translation manner; however, instead of translating from one language to another, they \"translate\" the input sequence (question) into the output sequence (answer) by sequentially generating text elements (usually words). This particular family of ML approaches is called sequence-to-sequence (abbreviated as seq2 seq). Seq2 seq chatbots can be created using either statistical machine translation (SML) or the recently popular neural machine translation (NMT) approaches. The pioneering work [12], describing how the SMT approach was applied to ~1.3 million conversations from the Twitter, demonstrated promising results and encouraged other researchers to continue working on generative chatbot problems. Neural approaches typically employ the encoder-decoder architecture, where the encoder sequentially reads the input text (question) and encodes it into a fixed-length context vector and the decoder sequentially outputs the text (answer) after reading the context vector. The encoder-decoder approaches learn to generate answers based on either the isolated question-answer (QA) pairs or on the whole conversation. The majority of approaches reuse (or slightly modify) the encoder-decoder architecture (described in Reference [13]) that was initially offered for NMT; both encoder and decoder are composed of long short-term memory (LSTM) networks applied on word2 vec embeddings [14]. The authors in Reference [15] applied their seq2 seq model to two benchmark English datasets: the closed-domain Information Technology (IT) Helpdesk Troubleshooting dataset and the open-domain OpenSubtitles dataset. A subjective evaluation of their system demonstrated its superiority over the Cleverbot (a popular chatterbot application). Similar research was performed in Reference [16]; however, instead of the English language, the authors applied the seq2 seq method to a Chinese dataset, containing ~1 million QA pairs taken from Chinese online forums. The performed subjective evaluation proved that the offered approach achieved good results in modeling the responding style of a human. The authors in Reference [17] used an encoder-decoder architecture enhanced with an attention mechanism. Their method was successfully applied to a mixture of words and syllables as encoding/decoding units. Two Korean corpora were used to train this model: the larger non-dialogue corpus captured the Korean language model and the smaller dialogue corpus (containing ~0.5 million sentence pairs) collected from mobile chat rooms was used to train the dialogue. The research in Reference [18] presented a conversational model focusing on previous queries/questions. They offered a hierarchical recurrent encoder-decoder neural-based approach that considers the history (i.e., sequences of words for each query and sequence of queries) of previously submitted queries, which was successfully trained on ~0.4 million English queries, demonstratingsufficient performance. Some researchers went even further by offering solutions for how to generate responses for a whole conversation to be successful. Reference [19] described a seminal work toward the creation of a neural conversational model for the long-term success of dialogues.