# Development of Dialogue Management System for Banking Services
###### Abstract
It should be noted that although there some studies, on topics such as speech recognition, speech synthesis, and sentiment analysis of texts [1; 2; 3], that have been conducted in the Azerbaijan language, the development of dialogue systems has been less studied. Furthermore, there is a lack of language processing tools for the Azerbaijan language. Some multilingual tools, such as BERT, fastText, and Spacy, trained on clean datasets from sources such as Wikipedia and text corpuses from books; thus, they are not effective for conversational chat data, which contain plenty of misspellings. Most of the misspelling issues are caused due to the alphabetical differences between the Azerbaijan and English languages and the usage of the English keyboard layout while typing in Azerbaijan. This issue is explained in more detail in Sections 3 and 4. Additionally, multilingual tools do not show good performance on our dataset, as their training data is not related to the bank domain. For this reason, we comparatively analyzed different machine learning methods and chose an effective set of techniques and parameters for our dataset. We anticipate that our experimental study will have a huge impact in our domain and will provide a good foundation for the upcoming work for the agglutinative low-resource languages. In our project, we designed and evaluated different DMS pipelines with respect to the Azerbaijan conversational text data obtained from one of the leading retail banks in Azerbaijan, Unibank. However, semantics of the dataset, i.e., meanings of the Azerbaijan words, have not been considered in the research and did not affect data preparation steps. Used techniques, such as bag-of-words (BoW) representation, term frequency-inverse document frequency (TF-IDF) vectorization, word embeddings, machine learning (ML) algorithms, etc., can be applied to other languages as well. The results achieved using such techniques on the other languages also discussed in the work, but those datasets do not belong to the banking sector. In addition, since the Azerbaijan language belongs to the group of agglutinative languages, it is anticipated that these experiments will be effective in other agglutinative languages as well. We experimented with various combinations of NLU and DM modules to find the best suitable pipeline for our conversational dataset. In Section 2, we discussed the major modules of the DMS pipeline by showing what kind of research has been already conducted within the domain. Section 3 is dedicated to the explanation of the conversational dataset obtained from one of the leading retail banks of Azerbaijan, Unibank. There is given a general description of the data, the distribution of intentions, and difficulties of the language in the same section. In Section 4, we analyzed major methods for building Language Identification, Intent Detection, Entity Extractor, and Dialogue Management modules. We investigated and compared both built-in language identification methods, such as fastText, and custom machine learning models trained on a domain-based dataset. For intent detection we applied Dual Intent and Entity Transformer (DIET) and other traditional machine learning methods such as Naive Bayes (NB), K-Nearest Neighbor (KNN), Logistic Regression (LR), Support Vector Machine (SVM), and Neural Networks (NN) with TF-IDF, CountVectorizer, sub-word embedding features to find the best model for our dataset. The NLU set-ups are compared and followed by a DM module that contains a rule-based Policy to handle FAQs and chitchats, as well as a transformer embedding dialogue (TED) policy to handle more complex and unexpected dialogue inputs. In Section 5, we describe experimental results in detail. We conclude by discussing major findings and providing summary in Sections 5 and 6.