Conversational recommender systems have attracted immense attention recently. The most recent approaches rely on neural models trained recorded dialogs between humans, implementing an end-to-end learning process. These are commonly designed to generate responses given the user's utterances in natural language. One main challenge is that these generated both be appropriate for dialog context and...

Health is a state of total physical, mental, and social wellbeing. chatbots have been applied to this industry frequently in variety ofways the past, there still room for more inventive uses. Healthcareconversational AI use cases are flexible may be tailored industry. Patients might them gain additional knowledge about their disease, therapies that available, or even insurance coverage. Because...

In order for conversational AI systems to hold more natural and broad-ranging conversations, they will require much commonsense, including the ability identify unstated presumptions of their partners. For example, in command "If it snows at night then wake me up early because I don't want be late work" speaker relies on commonsense reasoning listener infer implicit presumption that wish woken o...

This paper describes the use of phrase-sized segments for the concatenative synthesis of conversational speech and discusses the differences in selection criteria that become necessary when the source corpus contains several years of conversational speech samples. It claims that naturalsounding conversational speech can be reproduced by use of such phrase-sized chunks for concatenation, and tha...

In our work with conversational recommender systems we have derived two dialogue strategies called interview and delivery. We explore the symmetry between preferential interview and traditional clarification questions, and arrive at basic interview and delivery strategies suitable for conversational recommender system implementations. The strategies are based on a corpus analysis of recommendat...