7 research outputs found
An Ensemble Model with Ranking for Social Dialogue
Open-domain social dialogue is one of the long-standing goals of Artificial
Intelligence. This year, the Amazon Alexa Prize challenge was announced for the
first time, where real customers get to rate systems developed by leading
universities worldwide. The aim of the challenge is to converse "coherently and
engagingly with humans on popular topics for 20 minutes". We describe our Alexa
Prize system (called 'Alana') consisting of an ensemble of bots, combining
rule-based and machine learning systems, and using a contextual ranking
mechanism to choose a system response. The ranker was trained on real user
feedback received during the competition, where we address the problem of how
to train on the noisy and sparse feedback obtained during the competition.Comment: NIPS 2017 Workshop on Conversational A
If I Hear You Correctly: Building and Evaluating Interview Chatbots with Active Listening Skills
Interview chatbots engage users in a text-based conversation to draw out
their views and opinions. It is, however, challenging to build effective
interview chatbots that can handle user free-text responses to open-ended
questions and deliver engaging user experience. As the first step, we are
investigating the feasibility and effectiveness of using publicly available,
practical AI technologies to build effective interview chatbots. To demonstrate
feasibility, we built a prototype scoped to enable interview chatbots with a
subset of active listening skills - the abilities to comprehend a user's input
and respond properly. To evaluate the effectiveness of our prototype, we
compared the performance of interview chatbots with or without active listening
skills on four common interview topics in a live evaluation with 206 users. Our
work presents practical design implications for building effective interview
chatbots, hybrid chatbot platforms, and empathetic chatbots beyond interview
tasks.Comment: Working draft. To appear in the ACM CHI Conference on Human Factors
in Computing Systems (CHI 2020
Issues in the Development of Conversation Dialog for Humanoid Nursing Partner Robots in Long-Term Care
The purpose of this chapter is to explore the issues of development of conversational dialog of robots for nursing, especially for long-term care, and to forecast humanoid nursing partner robots (HNRs) introduced into clinical practice. In order to satisfy the required performance of HNRs, it is important that anthropomorphic robots act with high-quality conversational dialogic functions. As for its hardware, by allowing independent range of action and degree of freedom, the burden of quality exerted in human-robot communication is reduced, thereby unburdening nurses and professional caregivers. Furthermore, it is critical to develop a friendlier type of robot by equipping it with non-verbal emotive expressions that older people can perceive. If these functions are conjoined, anthropomorphic intelligent robots will serve as possible instructors, particularly for rehabilitation and recreation activities of older people. In this way, more than ever before, the HNRs will play an active role in healthcare and in the welfare fields