Towards versatile conversations with data-driven dialog management and its integration in commercial platforms

Cañas, Pablo; Griol, David; Callejas, Zoraida

doi:10.1016/j.jocs.2021.101443

Cited by 5 publications

(4 citation statements)

References 23 publications

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“…Our proposal for statistical dialog management is described in [9]. The dialog model decides the next system response by employing a classification process that considers the set of pairs (system turn A i -user turn U i ) preceding the current one to decide the next system response.…”

Section: Dialog Managementmentioning

confidence: 99%

“…The classification function can be defined in different ways. In [9], we propose the use of artificial neural networks to improve the results of classifying the information in the DR and the current state of the dialog to select as output the following system response.…”

Section: Dialog Managementmentioning

confidence: 99%

“…CUIs are also used to make their services more engaging and increase customer satisfaction in various applications, such as making appointments and reservations, answering questions, e-government procedures, and support services.On the other hand, machine learning and deep learning methodologies have become part of many of the current intelligent systems [4,5,6]. These methodologies have traditionally been used in the field of conversational interfaces to develop automatic speech recognizers [7,8] and, more recently, to implement natural language understanding modules, natural language generators, and dialog management processes [4,9,10].…”

mentioning

confidence: 99%

“…On the other hand, machine learning and deep learning methodologies have become part of many of the current intelligent systems [4,5,6]. These methodologies have traditionally been used in the field of conversational interfaces to develop automatic speech recognizers [7,8] and, more recently, to implement natural language understanding modules, natural language generators, and dialog management processes [4,9,10].…”

mentioning

confidence: 99%

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A Proposal for Developing and Deploying Statistical Dialog Management in Commercial Conversational Platforms

Cañas

Griol²,

Callejas³

2022

Lecture Notes in Computer Science

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Conversational interfaces have recently become ubiquitous in the personal sphere by improving an individual's quality of life and industrial environments by automating services and their corresponding cost savings. However, designing the dialog model used by these interfaces to decide the following response is a hard-to-accomplish task for complex conversational interactions. This paper proposes a statistical-based dialog manager architecture, which provides flexibility to develop and maintain this module. Our proposal has been integrated using DialogFlow, a natural language understanding platform provided by Google to design conversational user interfaces. The proposed hybrid architecture has been assessed with a real use case for a train scheduling domain, proving that the user experience is highly valued and can be integrated into commercial setups.Conversational Interfaces (CUI) are systems that simulate interactive conversations with humans [1, 2, 3]. These systems are meant to display human-like characteristics and support the use of spontaneous natural language to interact with different purposes, such as performing transactions, responding to questions, or chatting.These interfaces have become a key research subject for many companies, as they have understood the potential revenue of introducing these devices into society's mainstream. Virtual Personal Assistants (VPAs), such as Google Now, Apple's Siri, Amazon's Alexa, or Microsoft's Cortana, are the most representative examples. These VPAs are used for a wide variety of tasks, from setting the alarm to updating the calendar, passing through getting directions, finding the nearest restaurants or stores, or even planning a recipe or reporting the news. CUIs are also used to make their services more engaging and increase customer satisfaction in various applications, such as making appointments and reservations, answering questions, e-government procedures, and support services.On the other hand, machine learning and deep learning methodologies have become part of many of the current intelligent systems [4,5,6]. These methodologies have traditionally been used in the field of conversational interfaces to develop automatic speech recognizers [7,8] and, more recently, to implement natural language understanding modules, natural language generators, and dialog management processes [4,9,10].

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Section: Dialog Managementmentioning

confidence: 99%

Section: Dialog Managementmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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A Proposal for Developing and Deploying Statistical Dialog Management in Commercial Conversational Platforms

Cañas

Griol²,

Callejas³

2022

Lecture Notes in Computer Science

Self Cite

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Business chatbots with deep learning technologies: state-of-the-art, taxonomies, and future research directions

Zhang,

Lau,

David Xu

et al. 2024

Artif Intell Rev

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With the support of advanced hardware and software technology, Artificial Intelligence (AI) techniques, especially the increasing number of deep learning algorithms, have spawned the popularization of online intelligent services and accelerated the contemporary development and applications of chatbot systems. The promise of providing 24/7 uninterrupted business services and minimizing workforce costs has made business chatbots a hot topic due to the impact of the pandemic. It has attracted considerable attention from academic researchers and business practitioners. However, a thorough technical review of advanced chatbot technologies and their relevance and applications to various business domains is rare in the literature. The main contribution of this review article is the critical analysis of various chatbot development approaches and the underlying deep learning computational methods in the context of some business applications. We first conceptualize current business chatbot architectures and illustrate the technical characteristics of two common structures. Next, we explore the mainstream deep learning technologies in chatbot design from the perspective of computational methods and usages. Then, we propose a new framework to classify chatbot construction architectures and differentiate the traditional retrieval-based and generation-based chatbots in terms of the modern pipeline and end-to-end structures. Finally, we highlight future research directions for business chatbots to enable researchers to devote their efforts to the most promising research topics and commercial scenarios and for practitioners to benefit from realizing the trend in business chatbot development and applications.

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Optimization of frequent item set mining parallelization algorithm based on spark platform

Fan,

Jiabin,

Sheng

2024

Discov Computing

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Towards versatile conversations with data-driven dialog management and its integration in commercial platforms

Cited by 5 publications

References 23 publications

A Proposal for Developing and Deploying Statistical Dialog Management in Commercial Conversational Platforms

A Proposal for Developing and Deploying Statistical Dialog Management in Commercial Conversational Platforms

Business chatbots with deep learning technologies: state-of-the-art, taxonomies, and future research directions

Optimization of frequent item set mining parallelization algorithm based on spark platform

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