Title: Deep Reinforcement Learning for Inquiry Dialog Policies with Logical Formula Embeddings,
Abstract: This paper is the first attempt to learn the policy of an inquiry dialog
system (IDS) by using deep reinforcement learning (DRL). Most IDS frameworks
represent dialog states and dialog acts with logical formulae. In order to make
learning inquiry dialog policies more effective, we introduce a logical formula
embedding framework based on a recursive neural network. The results of
experiments to evaluate the effect of 1) the DRL and 2) the logical formula
embedding framework show that the combination of the two are as effective or
even better than existing rule-based methods for inquiry dialog policies.