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Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System

Authors: Yixuan Su, Lei Shu, Elman Mansimov, Arshit Gupta, Deng Cai, Yi-An Lai, and Yi Zhang

Code our PPTOD paper: Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System

Introduction:

Pre-trained language models have been recently shown to benefit task-oriented dialogue (TOD) systems. Despite their success, existing methods often formulate this task as a cascaded generation problem which can lead to error accumulation across different sub-tasks and greater data annotation overhead. In this study, we present PPTOD, a unified model that seamlessly supports both task-oriented dialogue understanding and response generation in a plug-and-play fashion. In addition, we introduce a new dialogue multi-task pre-training strategy that allows the model to learn the primary TOD task completion skills from heterogeneous dialog corpora. We extensively test our model on three benchmark TOD tasks, including end-to-end dialogue modelling, dialogue state tracking, and intent classification. Results show that PPTOD creates new state-of-the-art on all evaluated tasks in both full training and low-resource scenarios. Furthermore, comparisons against previous SOTA methods show that the responses generated by PPTOD are more factually correct and semantically coherent as judged by human annotators.

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1. Citation

If you find our paper and resources useful, please kindly cite our paper:

  @misc{su2021multitask,
        title={Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System}, 
        author={Yixuan Su and Lei Shu and Elman Mansimov and Arshit Gupta and Deng Cai and Yi-An Lai and Yi Zhang},
        year={2021},
        eprint={2109.14739},
        archivePrefix={arXiv},
        primaryClass={cs.CL}
  }

2. Environment Setup:

pip3 install -r requirements.txt
python -m spacy download en_core_web_sm

3. PPTOD Checkpoints:

You can download checkpoints of PPTOD with different configurations here.

PPTOD-small PPTOD-base PPTOD-large
here here here

To use PPTOD, you should download the checkpoint you want and unzip it in the ./checkpoints directory.

Alternatively, you can run the following commands to download the PPTOD checkpoints.

(1) Downloading Pre-trained PPTOD-small Checkpoint:

cd checkpoints
chmod +x ./download_pptod_small.sh
./download_pptod_small.sh

(2) Downloading Pre-trained PPTOD-base Checkpoint:

cd checkpoints
chmod +x ./download_pptod_base.sh
./download_pptod_base.sh

(3) Downloading Pre-trained PPTOD-large Checkpoint:

cd checkpoints
chmod +x ./download_pptod_large.sh
./download_pptod_large.sh

4. Data Preparation:

The detailed instruction for preparing the pre-training corpora and the data of downstream TOD tasks are provided in the ./data folder.

5. Dialogue Multi-Task Pre-training:

To pre-train a PPTOD model from scratch, please refer to details provided in ./Pretraining directory.

6. Benchmark TOD Tasks:

(1) End-to-End Dialogue Modelling:

To perform End-to-End Dialogue Modelling using PPTOD, please refer to details provided in ./E2E_TOD directory.

(2) Dialogue State Tracking:

To perform Dialogue State Tracking using PPTOD, please refer to details provided in ./DST directory.

(3) Intent Classification:

To perform Intent Classification using PPTOD, please refer to details provided in ./IC directory.

Security

See CONTRIBUTING for more information.

License

This project is licensed under the Apache-2.0 License.

pptod's People

Contributors

yxuansu avatar amazon-auto avatar

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