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 Azerbaijani, 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.

A Novel Methodology for Developing Troubleshooting Chatbots Applied to ATM Technical Maintenance Support

“…Several scholars have proposed and evaluated dialogue systems in the banking industry to improve customer service and satisfaction. Rustamov et al [59] conducted the development and evaluation of several dialog management pipelines to verify the most suitable one for application in a banking context. The primary objective of this research was to investigate the dialogue manager and NLU components.…”

Automatic explanation of the classification of Spanish legal judgments in jurisdiction-dependent law categories with tree estimators

“…Recent improvements in Natural Language Processing (nlp) technologies and Machine Learning (ml) algorithms have allowed solving an ample variety of problems such as summarization (Trappey et al, 2020;Gambhir & Gupta, 2017), user profiling (Tellez et al, 2018;Flores et al, 2022) and decision making (Trappey et al, 2020;Rana & Varshney, 2021). They have jointly contributed to intelligent conversational assistants (Rustamov et al, 2021;Hasal et al, 2021) and sentiment and emotion analysis systems (Kastrati et al, 2021;Tao et al, 2019). More closely related to our work are text classification problems (Kowsari et al, 2019;Hettiarachchi et al, 2022;Škrlj et al, 2021), and particularly, those in the legal field (Medvedeva et al, 2020;Dyevre, 2021b,a).…”