Deep Learning for Database Mapping and Asking Clarification Questions in Dialogue Systems

Mandy Korpusik, James R. Glass

Abstract: A dialogue system will often ask followup clarification questions when interacting with a user if the agent is unsure how to respond. In this new study, we explore deep reinforcement learning (RL) for asking followup questions when a user records a meal description, and the system needs to narrow down the options for which foods the person has eaten. We build off of prior work in which we use novel convolutional neural network models to bypass the standard feature engineering used in dialogue systems to handle the text mismatch between natural language user queries and structured database entries, demonstrating that our model learns semantically meaningful embedding representations of natural language. In this new nutrition domain, the followup clarification questions consist of possible attributes for each food that was consumed; for example, if the user drinks a cup of milk, the system should ask about the percent milkfat. We investigate an RL agent to dynamically follow up with the user, which we compare to rule-based and entropy-based methods. On a held-out test set, assuming the followup questions are answered correctly, deep RL significantly boosts top five food recall from 54.9% without followup to 89.0%. We also demonstrate that a hybrid RL model achieves the best perceived naturalness ratings in a human evaluation.