Open AccessLightBlue: Nurture Your Personal Chatbot
Author(s) -
Xiang Zhang,
Yan Liu,
Chen Gong,
Sheng-hua Zhong
Publication year - 2022
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5121/csit.2022.120401
Subject(s) - nature versus nurture , chatbot , computer science , human–computer interaction , domain (mathematical analysis) , term (time) , world wide web , sociology , mathematical analysis , physics , mathematics , quantum mechanics , anthropology
Chatbot has long been an important research topic in artificial intelligence and attracts lots of attention recently. Despite significant advancements in language ability, the interactions between users and chatbots are rather generic, short-term, and transnational. It has always been challenging to develop truly personal chatbots and even more difficult to establish longterm, affective connections. This paper first brings up “nurture” as a new interaction mode with chatbots. We introduce the nurture framework and accordingly design the learning algorithm and nurture functions. Then we present LightBlue – a platform that allows non-professionals to nurture personal chatbots from scratch. Experiments on both closed- and open-domain tasks validate the proposed framework and demonstrate a promising method for facilitating long-term interaction between users and chatbots.