A review of natural language processing in contact centre automation

Contact centres have been highly valued by organizations for a long time. However, the COVID-19 pandemic has highlighted their critical importance in ensuring business continuity, economic activity, and quality customer support. The pandemic has led to an increase in customer inquiries related to payment extensions, cancellations, and stock inquiries, each with varying degrees of urgency. To address this challenge, organizations have taken the opportunity to re-evaluate the function of contact centres and explore innovative solutions. Next-generation platforms that incorporate machine learning techniques and natural language processing, such as self-service voice portals and chatbots, are being implemented to enhance customer service. These platforms offer robust features that equip customer agents with the necessary tools to provide exceptional customer support. Through an extensive review of existing literature, this paper aims to uncover research gaps and explore the advantages of transitioning to a contact centre that utilizes natural language solutions as the norm. Additionally, we will examine the major challenges faced by contact centre organizations and offer reco

On the Development of Adaptive and User-Centred Interactive Multimodal Interfaces

Multimodal systems have attained increased attention in recent years, which has made possible important improvements in the technologies for recognition, processing, and generation of multimodal information. However, there are still many issues related to multimodality which are not clear, for example, the principles that make it possible to resemble human-human multimodal communication. This chapter focuses on some of the most important challenges that researchers have recently envisioned for future multimodal interfaces. It also describes current efforts to develop intelligent, adaptive, proactive, portable and affective multimodal interfaces

Call Center Experience Optimization: A Case for a Virtual Predictive Queue

The evolution of the call center into contact centers and the growth of their use in providing customer-facing service by many companies has brought considerable capabilities in maintaining customer relationships but it also has brought challenges in providing quality service when call volumes are high. Limited in their ability to provide service at all times to all customers, companies are forced to balance the costs associated with hiring more customer service representatives and the quality of service provided by a fewer number. A primary challenge when there are not enough customer service representatives to engage the volume of callers in a timely manner is the significant wait times that can be experienced by many customers. Normally, callers are handled in accordance with a first-come, first-served policy with exceptions being skill-based routing to those customer service representatives with specialized skills. A proposed call center infrastructure framework called a Virtual Predictive Queue (VPQ) can allow some customers to benefit from a shorter call queue wait time. This proposed system can be implemented within a call center’s Automatic Call Distribution (ACD) device associated with computer telephony integration (CTI) and theoretically will not violate a first-come, first served policy

Reinforcement Learning

Brains rule the world, and brain-like computation is increasingly used in computers and electronic devices. Brain-like computation is about processing and interpreting data or directly putting forward and performing actions. Learning is a very important aspect. This book is on reinforcement learning which involves performing actions to achieve a goal. The first 11 chapters of this book describe and extend the scope of reinforcement learning. The remaining 11 chapters show that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional, hand-designed, non-learning controllers. As learning computers can deal with technical complexities, the tasks of human operators remain to specify goals on increasingly higher levels. This book shows that reinforcement learning is a very dynamic area in terms of theory and applications and it shall stimulate and encourage new research in this field

The SEMAINE API : a component integration framework for a naturally interacting and emotionally competent embodied conversational agent

The present thesis addresses the topic area of Embodied Conversational Agents (ECAs) with capabilities for natural interaction with a human user and emotional competence with respect to the perception and generation of emotional expressivity. The focus is on the technological underpinnings that facilitate the implementation of a real-time system with these capabilities, built from re-usable components. The thesis comprises three main contributions. First, it describes a new component integration framework, the SEMAINE API, which makes it easy to build emotion-oriented systems from components which interact with one another using standard and pre-standard XML representations. Second, it presents a prepare-and-trigger system architecture which substantially speeds up the time to animation for system utterances that can be pre-planned. Third, it reports on the W3C Emotion Markup Language, an upcoming web standard for representing emotions in technological systems. We assess critical aspects of system performance, showing that the framework provides a good basis for implementing real-time interactive ECA systems, and illustrate by means of three examples that the SEMAINE API makes it is easy to build new emotion-oriented systems from new and existing components.Die vorliegende Dissertation behandelt das Thema der virtuellen Agenten mit Fähigkeiten zur natürlichen Benutzer-Interaktion sowie emotionaler Kompetenz bzgl. der Wahrnehmung und Generierung emotionalen Ausdrucks. Der Schwerpunkt der Arbeit liegt auf den technologischen Grundlagen für die Implementierung eines echtzeitfähigen Systems mit diesen Fähigkeiten, das aus wiederverwendbaren Komponenten erstellt werden kann. Die Arbeit umfasst drei Kernaspekte. Zum Einen beschreibt sie ein neues Framework zur Komponenten-Integration, die SEMAINE API: Diese erleichtert die Erstellung von Emotions-orientierten Systemen aus Komponenten, die untereinander mittels Standard- oder Prä-Standard-Repräsentationen kommunizieren. Zweitens wird eine Systemarchitektur vorgestellt, welche Vorbereitung und Auslösung von Systemverhalten entkoppelt und so zu einer substanziellen Beschleunigung der Generierungszeit führt, wenn Systemäußerungen im Voraus geplant werden können. Drittens beschreibt die Arbeit die W3C Emotion Markup Language, einen werdenden Web-Standard zur Repräsentation von Emotionen in technologischen Systemen. Es werden kritische Aspekte der Systemperformanz untersucht, wodurch gezeigt wird, dass das Framework eine gute Basis für die Implementierung echtzeitfähiger interaktiver Agentensysteme darstellt. Anhand von drei Beispielen wird illustriert, dass mit der SEMAINE API leicht neue Emotions-orientierte Systeme aus neuen und existierenden Komponenten erstellt werden können

Machine Learning

Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience

Efficient model learning for dialog management

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 118-122).Partially Observable Markov Decision Processes (POMDPs) have succeeded in many planning domains because they can optimally trade between actions that will increase an agent's knowledge about its environment and actions that will increase an agent's reward. However, POMDPs are defined with a large number of parameters which are difficult to specify from domain knowledge, and gathering enough data to specify the parameters a priori may be expensive. This work develops several efficient algorithms for learning the POMDP parameters online and demonstrates them on dialog manager for a robotic wheelchair. In particular, we show how a combination of specialized queries ("meta-actions") can enable us to create a robust dialog manager that avoids the pitfalls in other POMDP-learning approaches. The dialog manager's ability to reason about its uncertainty -- and take advantage of low-risk opportunities to reduce that uncertainty -- leads to more robust policy learning.by Final Doshi.S.M

Interim research assessment 2003-2005 - Computer Science

This report primarily serves as a source of information for the 2007 Interim Research Assessment Committee for Computer Science at the three technical universities in the Netherlands. The report also provides information for others interested in our research activities

Effectiveness of OPC for systems integration in the process control information architecture

A Process is defined as the progression to some particular end or objective through a logical and orderly sequence of events. Various devices (e.g., actuators, limit switches, motors, sensors, etc.) play a significant role in making sure that the process attains its objective (e.g., maintaining the furnace temperature within an acceptable limit). To do these things effectively, manufacturers need to access data from the plant floor or devices and integrate those into their control applications, which maybe one of the off the shelf tools such as Supervisory Control and Data Acquisition (SCADA), Distributed Control System (DCS), or Programmable Logic Controllers (PLC). A number of vendors have devised their own Data Acquisition Networks or Process Control Architectures (e.g., PROFIBUS, DEVICENET, INTERBUS, ETHERNET I/P, etc.) that claim to be open to or interoperable with a number of third party devices or products that make process data available to the Process or Business Management level. In reality this is far from what it is claimed to be. Due to the problem of interoperability, a manufacturer is forced to be bound, either with the solutions provided by a single vendor or with the writing of a driver for each hardware device that is accessed by a process application. Today\u27s manufacturers are looking for advanced distributed object technologies that allow for seamless exchange of information across plant networks as a means of integrating the islands of automation that exist in their manufacturing operations. OLE for Process Control (OPC) works to significantly reduce the time, cost, and effort required in writing custom interfaces for hundreds of different intelligent devices and networks in use today. The objective of this thesis is to explore the OLE for Process Control (OPC) technology in depth by highlighting its need in industry and by using the OPC technology in an application in which data from a process controlled by Siemens Simatic S7 PLC are shared with a client application running in LabVTEW6i