What Do You Mean I'm Funny? Personalizing the Joke Skill of a Voice-Controlled Virtual Assistant
This work addresses the challenge of personalizing humor for users of voice assistants, though it is incremental as it builds on existing NLP and deep learning techniques.
The paper tackled the problem of personalizing joke delivery in voice-controlled virtual assistants to enhance user engagement, finding that models using implicit feedback labels outperformed heuristic methods and improved user satisfaction in real-world tests.
A considerable part of the success experienced by Voice-controlled virtual assistants (VVA) is due to the emotional and personalized experience they deliver, with humor being a key component in providing an engaging interaction. In this paper we describe methods used to improve the joke skill of a VVA through personalization. The first method, based on traditional NLP techniques, is robust and scalable. The others combine self-attentional network and multi-task learning to obtain better results, at the cost of added complexity. A significant challenge facing these systems is the lack of explicit user feedback needed to provide labels for the models. Instead, we explore the use of two implicit feedback-based labelling strategies. All models were evaluated on real production data. Online results show that models trained on any of the considered labels outperform a heuristic method, presenting a positive real-world impact on user satisfaction. Offline results suggest that the deep-learning approaches can improve the joke experience with respect to the other considered methods.