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Inspired by studies on the overwhelming presence of experience-sharing in humanhuman conversations, Emora, the social chatbot developed by Emory University, aims to bring such experience-focused interaction to the current field of conversational AI. The traditional approach of information-sharing topic handlers is balanced with a focus on opinion-oriented exchanges that Emora delivers, and new conversational abilities are developed that support dialogues that consist of a collaborative understanding and learning process of the partner's life experiences. We present a curated dialogue system that leverages highly expressive natural language templates, powerful intent classification, and ontology resources to provide an engaging and interesting conversational experience to every user. * Team leads. The rest of the student authors are in alphabetical order. † Faculty advisor.3rd Proceedings of Alexa Prize (Alexa Prize 2019).

The increasing popularity of voice-based personal assistants provides new opportunities for conversational recommendation. One particularly interesting area is movie recommendation, which can benefit from an open-ended interaction with the user, through a natural conversation. We explore one promising direction for conversational recommendation: mapping a conversational user, for whom there is limited or no data available, to most similar external reviewers, whose preferences are known, by representing the conversation as a user's interest vector, and adapting collaborative filtering techniques to estimate the current user's preferences for new movies. We call our proposed method ConvExtr (Conversational Collaborative Filtering using External Data), which 1) infers a user's sentiment towards an entity from the conversation context, and 2) transforms the ratings of "similar" external reviewers to predict the current user's preferences. We implement these steps by adapting contextual sentiment prediction techniques, and domain adaptation, respectively. To evaluate our method, we develop and make available a finely annotated dataset of movie recommendation conversations, which we call MovieSent . Our results demonstrate that Con-vExtr can improve the accuracy of predicting users' ratings for new movies by exploiting conversation content and external data.