Abstract
Current language processing technologies allow the creation of conversational chatbot platforms. Even though artificial intelligence is still too immature to support satisfactory user experience in many mass market domains, conversational interfaces have found their way into ad hoc applications such as call centres and online shopping assistants. However, they have not been applied so far to social inclusion of elderly people, who are particularly vulnerable to the digital divide. Many of them relieve their loneliness with traditional media such as TV and radio, which are known to create a feeling of companionship. In this paper we present the EBER chatbot, designed to reduce the digital gap for the elderly. EBER reads news in the background and adapts its responses to the user’s mood. Its novelty lies in the concept of “intelligent radio”, according to which, instead of simplifying a digital information system to make it accessible to the elderly, a traditional channel they find familiar -background news- is augmented with interactions via voice dialogues. We make it possible by combining Artificial Intelligence Modelling Language, automatic Natural Language Generation and Sentiment Analysis. The system allows accessing digital content of interest by combining words extracted from user answers to chatbot questions with keywords extracted from the news items. This approach permits defining metrics of the abstraction capabilities of the users depending on a spatial representation of the word space. To prove the suitability of the proposed solution we present results of real experiments conducted with elderly people that provided valuable insights. Our approach was considered satisfactory during the tests and improved the information search capabilities of the participants.
Highlights
Humans are innately social and need companionship [1], [2]
Social inclusion of elderly people is essential to improve their quality of life in our ageing society
When the Natural Language Generation (NLG) module is unable to extract keywords or phrases to generate coherent empathetic text, it selects positive or negative sentences from a set of predefined templates based on the knowledge of the Sentiment Analysis (SA) module
Summary
Humans are innately social and need companionship [1], [2]. Social inclusion of elderly people is essential to improve their quality of life in our ageing society. In the case of elderly people, several works have noted their low level of engagement due to the sometimes insuperable digital gap [3]–[5]. García-Méndez et al.: Entertainment Chatbot for Digital Inclusion senior population [6] This population typically alleviates solitude by continuously consuming traditional broadcast media such as television and radio [7]. The system allows accessing digital content of interest, by combining words extracted from user answers to chatbot questions with keywords extracted from the news items. This permits defining metrics of the abstraction capabilities of the users depending on a spatial representation of the word space.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
