Codes and Data for ACL2023 Submission

This repository contains the official codes for our paper at ACL 2023: EM Pre-training for Multi-party Dialogue Response Generation.

Environmental Settings

• GPU: TITAN RTX 24G
• CUDA: 11.7
• Python: 3.9.12
• Pytorch: 1.12.0
• Other dependencies: see requirements.txt

We strongly recommand that you run our codes on the same settings with Docker or Anaconda to ensure reproducibility. You can run $ pip3 install -r requirements.txt to install other dependencies.

Running

First, you should download the pre-trained model from the Google Drive, then create a new folder named pretrain_models under the same path of this README file and put the downloaded model in this folder (./pretrain_models/mpdrg.pth).

Then, you should unzip the data.zip file to get the datset.

After that, you can run the following command to fine-tune the pre-trained model on the Ubuntu IRC benchmark:

$ bash run_finetune_ubuntu.sh [GPU_ID]

Citation

If you find our paper and repository useful, please cite us in your paper:

@inproceedings{li-zhao-2023-em,
    title = "{EM} Pre-training for Multi-party Dialogue Response Generation",
    author = "Li, Yiyang  and
      Zhao, Hai",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.7",
    pages = "92--103",
}