What Do You Mean I'm Funny? Personalizing the Joke Skill of a Voice-Controlled Virtual Assistant

TL;DR

This paper explores personalized joke-telling in voice assistants using NLP and deep learning, demonstrating improved user satisfaction through implicit feedback-based models evaluated on real data.

Contribution

It introduces novel personalization methods for joke-telling in voice assistants, combining traditional NLP with deep learning techniques and implicit feedback strategies.

Findings

Deep learning models outperform heuristic methods in offline evaluations.

Models trained on implicit feedback labels improve user satisfaction in real-world tests.

Personalization enhances the humor experience in voice-controlled virtual assistants.

Abstract

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.