DialogUSR: Complex Dialogue Utterance Splitting and Reformulation for Multiple Intent Detection

While interacting with chatbots, users may elicit multiple intents in a single dialogue utterance.Instead of training a dedicated multiintent detection model, we propose DialogUSR, a dialogue utterance splitting and reformulation task that first splits multi-intent user query into several single-intent sub-queries and then recovers all the coreferred and omitted information in the sub-queries.DialogUSR can serve as a plug-in and domain-agnostic module that empowers the multi-intent detection for the deployed chatbots with minimal efforts.We collect a high-quality naturally occurring dataset that covers 23 domains with a multi-step crowdsouring procedure.To benchmark the proposed dataset, we propose multiple action-based generative models that involve end-to-end and twostage training, and conduct in-depth analyses on the pros and cons of the proposed baselines. * Equal contribution.