Answering Student Queries with a Supervised Memory Conversational Agent

Abstract

This paper describes a discussion-bot that provides answers to students’ questions about the Data Science master program at the University of Lyon 1. Based on a seq2seq architecture combined with a supervised memory module, the bot identifies the questioner’s interest and encodes relevant information from the past conversation to provide personalized answers. A dialogue generator based on hand-crafted dialogues was built to train our model on these synthetic dialogues. The agent and its memory are adaptable to another context by modifying the intention database of the generator. The model was deployed and the results show that the discussion-bot meets most students’ learning requests. We discuss further directions that might be taken to increase the model's effectiveness.