multi_intent_2022

Public repository associated with DialogUSR: Complex Dialogue Utterance Splitting and Reformulation for Multiple Intent Detection, Findings of EMNLP 2022

Data

The original unsplit data is at here, while the data for single stage is at data/MultiIntent_second_stage_once/, two stage at data/MultiIntent_second_stage_once/, and causal two stage at data/MultiIntent_second_stage_causal/

Setup

Install evaluation tool and transformers.

sh scripts/setup.sh

Training

Single stage

sh scripts/train_multi_intent.sh

Two stage

sh scripts/train_two_stage.sh

Citation

@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",
    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",
    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.",
}