# Development of Dialogue Management System for Banking Services
###### Abstract  
Rapid increase in conversational AI and user chat data lead to intensive development of dialogue management systems (DMS) for various industries. Yet, for low-resource languages, such as Azerbaijan, very little research has been conducted. The main purpose of this work is to experiment with various DMS pipeline set-ups to decide on the most appropriate natural language understanding and dialogue manager settings. In our project, we designed and evaluated different DMS pipelines with respect to the conversational text data obtained from one of the leading retail banks in Azerbaijan. In the work, the main two components of DMS--Natural language Understanding (NLU) and Dialogue Manager--have been investigated. In the first step of NLU, we utilized a language identification (LI) component for language detection. We investigated both built-in LI methods such as fastText and custom machine learning (ML) models trained on the domain-based dataset. The second step of the work was a comparison of the classic ML classifiers (logistic regression, neural networks, and SVM) and Dual Intent and Entity Transformer (DIET) architecture for user intention detection. In these experiments we used different combinations of feature extractors such as CountVectorizer, Term Frequency-Inverse Document Frequency (TF-IDF) Vectorizer, and word embeddings for both word and character n-gram based tokens. To extract important information from the text messages, Named Entity Extraction (NER) component was added to the pipeline. The best NER model was chosen among conditional random fields (CRF) tagger, deep neural networks (DNN), models and build in entity extraction component inside DIET architecture. Obtained entity tags fed to the Dialogue Management module as features. All NLU set-ups were followed by the Dialogue Management 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. As a result, we suggest a DMS pipeline for a financial assistant, which is capable of identifying intentions, named entities, and a language of text followed by policies that allow generating a proper response (based on the designed dialogues) and suggesting the best next action. dialogue management systems; low-resource language; conversational artificial agent; intent classification; conversational chat-bot +
Footnote †: journal: Medicalorders, etc.). The latter direction relies on carefully designed conversations and user input processing and extremely reduces the load on call centers that receive a vast amount of repetitive questions. Both automation aims have to be backed up by solid natural language processing techniques that allow understanding of the domain language and generating a reasonable answer to user requests. Financial assistants are considered a part of conversational software where the biggest challenge is to develop machine-learning based dialogue management systems (DMS) and support it with powerful language-understanding and language-generation modules. As a result, modern DMS architectures require several fundamental modules within the pipeline:  
* Natural Language Understanding (NLU)--performs users' input processing and handles slot-values and sentence intention and entities;
* Dialogue Manager (DM)--maintains the dialogue state and suggests the next action to be taken or a sentence to be responded to;
* Natural Language Generator--receives a specific communication act from the DM and generates a natural language response. In our work, natural responses are ready sentences that have been obtained from chat dialogues and are based on the NLU and DM steps being produced as a response during the conversation with the chat-bot. Therefore, we mostly focused on NLU and DM modules in the paper.