Evaluation of abstraction capabilities and detection of discomfort with a newscaster chatbot for entertaining elderly users
| Autor: | Enrique Costa-Montenegro, Francisco J. González-Castaño, Silvia García-Méndez, Francisco de Arriba-Pérez |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Male
5802.01 Educación de Adultos Computer science TP1-1185 computer.software_genre Biochemistry Chatbot Article Analytical Chemistry Personalization active and healthy ageing robotics and intelligent systems Human–computer interaction intelligent conversational system smart living application Feature (machine learning) Humans Electrical and Electronic Engineering natural language processing Instrumentation Abstraction (linguistics) Digital inclusion Aged Language Recall Chemical technology Communication Natural language generation artificial intelligence Atomic and Molecular Physics and Optics 5701.04 Lingüística Informatizada machine learning 1203.04 Inteligencia Artificial human computer interaction Female 3325 Tecnología de las Telecomunicaciones computer |
| Zdroj: | Investigo. Repositorio Institucional de la Universidade de Vigo Universidade de Vigo (UVigo) Sensors (Basel, Switzerland) Sensors, Vol 21, Iss 5515, p 5515 (2021) Sensors Volume 21 Issue 16 |
| Popis: | We recently proposed a novel intelligent newscaster chatbot for digital inclusion. Its controlled dialogue stages (consisting of sequences of questions that are generated with hybrid Natural Language Generation techniques based on the content) support entertaining personalisation, where user interest is estimated by analysing the sentiment of his/her answers. A differential feature of our approach is its automatic and transparent monitoring of the abstraction skills of the target users. In this work we improve the chatbot by introducing enhanced monitoring metrics based on the distance of the user responses to an accurate characterisation of the news content. We then evaluate abstraction capabilities depending on user sentiment about the news and propose a Machine Learning model to detect users that experience discomfort with precision, recall, F1 and accuracy levels over 80%. Xunta de Galicia | Ref. GRC2018/053 |
| Databáze: | OpenAIRE |
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