Simulated Chats for Building Dialog Systems: Learning to Generate Conversations from Instructions

Abstract

Popular dialog data sets such as MultiWOZ (Budzianowski et al., 2018) are created by providing crowd workers an instruction, expressed in natural language, that describes the task to be accomplished. Crowd workers play the role of a user and an agent to generate dialogs to accomplish tasks involving booking restaurant tables, calling a taxi etc. In this paper, we present a data creation strategy that uses the pre-trained language model, GPT2 (Radford et al., 2018), to simulate the interaction between crowd workers by creating a user bot and an agent bot. We train the simulators using a smaller percentage of actual crowd-generated conversations and their corresponding instructions. We demonstrate that by using the simulated data, we achieve significant improvements in low-resource settings on two publicly available datasets - MultiWOZ dataset (Budzianowski et al., 2018) and the Persona chat dataset (Zhang et al., 2018a).