Deep Learning Chatbots for Sustainable Application in E-health
A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Health, Well-Being and Sustainability".
Deadline for manuscript submissions: closed (24 April 2021) | Viewed by 532
Special Issue Editors
Interests: big data; social network analysis; data mining; deep learning
Interests: big data; social network analysis
Special Issue Information
Dear Colleagues,
In the last several years conversational agents (chatbots), a branch of natural language processing (NLP), have been widely analyzed due to the high number of business applications as well as applications in customer support or automated FAQs and personal care services. A chatbot is a conversational software system designed to emulate the communication skills of a human being, and automatically interacts with a user. Conversational agents have gained increasing attention from research and industrial fields. In particular, one of the main challenges concerns the semantic relevance between a user’s query and the chatbot’s response. The first approaches for modeling the chatbots’ responses to are rules-based methods that make it possible to produce satisfying responses when the target domain is limited (e.g., a model trained only for sports or medical conversations) in which it is not allowed to make grammatical or semantic mistakes during its service. In fact, chatbot models used in public services are typically based on rules or information retrieval techniques to ensure quality and adequate responses to users.
However, the development of big data and data mining techniques bring new opportunities for improving the performance of conversational agents exploiting the analysis of large datasets, especially exploiting deep learning techniques.
This Special Issue aims to study how data science methodologies can be applied to deep learning chatbots.
In particular, the main topics that this Special Issue should cover include but are not limited to:
- Deep learning chatbot for different applications (i.e., eHealth, customer care, Q&A);
- Decision support systems based on deep learning chatbots;
- NLP methods for deep learning chatbots;
- Deep conversational systems/interfaces and question answering;
- Machine translation.
Research and review articles are therefore invited to be submitted to this Special Issue to contribute to and highlight computing science methods for deep learning chatbots.
Dr. Giancarlo Sperlì
Mr. Antonino Ferraro
Mr. Antonio Galli
Guest Editors
Manuscript Submission Information
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Keywords
- big data
- deep learning
- decoder-encoder
- RNN
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