Title: Contextual memory bandit for pro-active dialog engagement. Abstract: An objective of pro-activity in dialog systems is to enhance the usability of conversational
agents by enabling them to initiate conversation on their own. While
dialog systems have become increasingly popular during the last couple of years,
current task oriented dialog systems are still mainly reactive and users tend to
initiate conversations. In this paper, we propose to introduce the paradigm of contextual
bandits as framework for pro-active dialog systems. Contextual bandits
have been the model of choice for the problem of reward maximization with partial
feedback since they fit well to the task description. As a second contribution,
we introduce and explore the notion of memory into this paradigm. We propose
two differentiable memory models that act as parts of the parametric reward estimation
function. The first one, Convolutional Selective Memory Networks, uses
a selection of past interactions as part of the decision support. The second model,
called Contextual Attentive Memory Network, implements a differentiable attention
mechanism over the past interactions of the agent. The goal is to generalize
the classic model of contextual bandits to settings where temporal information
needs to be incorporated and leveraged in a learnable manner. Finally, we illustrate
the usability and performance of our model for building a pro-active mobile
assistant through an extensive set of experiments.