@inproceedings{meng-etal-2022-dialogusr,
title = "{D}ialog{USR}: Complex Dialogue Utterance Splitting and Reformulation for Multiple Intent Detection",
author = "Meng, Haoran and
Xin, Zheng and
Liu, Tianyu and
Wang, Zizhen and
Feng, He and
Lin, Binghuai and
Zhao, Xuemin and
Cao, Yunbo and
Sui, Zhifang",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.234",
doi = "10.18653/v1/2022.findings-emnlp.234",
pages = "3214--3229",
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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<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.</abstract>
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%0 Conference Proceedings
%T DialogUSR: Complex Dialogue Utterance Splitting and Reformulation for Multiple Intent Detection
%A Meng, Haoran
%A Xin, Zheng
%A Liu, Tianyu
%A Wang, Zizhen
%A Feng, He
%A Lin, Binghuai
%A Zhao, Xuemin
%A Cao, Yunbo
%A Sui, Zhifang
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F meng-etal-2022-dialogusr
%X 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.
%R 10.18653/v1/2022.findings-emnlp.234
%U https://aclanthology.org/2022.findings-emnlp.234
%U https://doi.org/10.18653/v1/2022.findings-emnlp.234
%P 3214-3229
Markdown (Informal)
[DialogUSR: Complex Dialogue Utterance Splitting and Reformulation for Multiple Intent Detection](https://aclanthology.org/2022.findings-emnlp.234) (Meng et al., Findings 2022)
ACL
- Haoran Meng, Zheng Xin, Tianyu Liu, Zizhen Wang, He Feng, Binghuai Lin, Xuemin Zhao, Yunbo Cao, and Zhifang Sui. 2022. DialogUSR: Complex Dialogue Utterance Splitting and Reformulation for Multiple Intent Detection. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 3214–3229, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.