Context Analysis of Customer Requests using a Hybrid Adaptive Neuro Fuzzy Inference System and Hidden Markov Models in the Natural Language Call Routing Problem

Rustamov, Samir; Mustafayev, Elshan; Clements, Mark A.

doi:10.1515/eng-2018-0008

Cited by 13 publications

(4 citation statements)

References 15 publications

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“…It can be noted that for a noisy dataset in the agglutinative language, classic ML models such as SVM and NN almost show the same performances as the transformer base algorithm. The only work on intent classification problem in the Azerbaijani language were in [17,18]. They applied Neuro-Fuzzy methods and achieved approximately 90% accuracy for the dataset with four intention classes; however, in our task, the number of intent classes is 63.…”

Section: Discussionmentioning

confidence: 98%

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Development of Dialogue Management System for Banking Services

2021

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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.

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Section: Discussionmentioning

confidence: 98%

“…The authors of [16] introduced Bidirectional Gated Recurrent Unit (BiGRU) where the current hidden state is used to tag an entity and the hidden state of the previous step is used to classify an intent. Another approach for user intention detection is Neuro-Fuzzy Inference systems in Call Routing Systems [17,18].…”

Section: Natural Language Understanding Modulementioning

confidence: 99%

Development of Dialogue Management System for Banking Services

2021

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“…NLP techniques seek to make the call routing process more convenient for customers using speech-enabled systems rather than closed-menu systems. The challenge, therefore, is to understand the query from the customer's own description, rather than using touch-tone or speech-enabled menus [16].…”

Section: Data-driven Contact Routingmentioning

confidence: 99%

“…Data-driven technologies enable contact centers to become more proactive, better assist customers, and enhance their interaction experience [15]. Customer support systems can leverage transactional data to better understand customer queries and provide self-service support to improve routing and employee productivity [14], [16]. Intelligent contact routing can reduce customer waiting time and improve contact resolution FIGURE 1.…”

Section: Introductionmentioning

confidence: 99%

Simulation Assessment of Data-Driven Channel Allocation and Contact Routing in Customer Support Systems

Grogan

Moazeni

2023

IEEE Open J. Syst. Eng.

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Data-driven operations management methods can transform company operations, respond rapidly to customer demands, and enable new business models. However, companies face the challenge of measuring and evaluating how new technology will impact operational processes. This paper takes a systems engineering approach to assess the tradeoffs of adopting data-driven mechanisms to improve operational processes in a multichannel customer support system. We investigate potential cost savings from two technology applications: classification methods to direct customers to efficient self-service communication channels and routing methods to match customers with agents based on the query type and available skill set. Discrete event simulation evaluates how new technology adoption affects system-level performance. What-if scenarios combine distinct configurations of customer classification mechanisms and available communication channels to evaluate the reduction in the total number of agents required to meet a target service quality level. Discussion includes practical examples of how operational managers could use experimental information to make strategic operational decisions when adopting data-driven technologies.INDEX TERMS Data-driven methods, Operations management, Simulation, Systems engineering.

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