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

• URL: http://arxiv.org/abs/2210.11279v1
• Date: Thu, 20 Oct 2022 13:56:35 GMT
• Title: DialogUSR: Complex Dialogue Utterance Splitting and Reformulation for Multiple Intent Detection
• Authors: Haoran Meng, Zheng Xin, Tianyu Liu, Zizhen Wang, He Feng, Binghuai Lin, Xuemin Zhao, Yunbo Cao and Zhifang Sui
• Abstract summary: Instead of training a dedicated multi-intent detection model, we propose DialogUSR. DialogUSR splits multi-intent user query into several single-intent sub-queries. It then recovers all the coreferred and omitted information in the sub-queries.
• Score: 27.787807111516706
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: While interacting with chatbots, users may elicit multiple intents in a single dialogue utterance. Instead of training a dedicated multi-intent 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 crowd-souring procedure. To benchmark the proposed dataset, we propose multiple action-based generative models that involve end-to-end and two-stage training, and conduct in-depth analyses on the pros and cons of the proposed baselines.

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