Hierarchical Recurrent Attention Networks For Context-Aware Education Chatbots

We propose a hierarchical network architecture for context-aware dialogue systems, that chooses which parts of the past conversation to focus on through a two-layer attention mechanism. The model can encode the parts of the historical dialog that are relevant to the current question to reason about the required response. We first assess the performance of our model on the Dialog bAbI task that involves a restaurant reservation system, where the goal is to book a table at a restaurant. We then train our model on a new hand-crafted dialogue data set, consisting of 7500 dialogues, to inform prospective students about the Data Science master program at University of Lyon.