Zero-shot Cross-lingual Transfer of Prompt-based Tuning with a Unified Multilingual Prompt

Implementation of the paper Zero-shot Cross-lingual Transfer of Prompt-based Tuning with a Unified Multilingual Prompt.

Acknowledgment

This implementation is based on the released source code of LM-BFF, and we appreciate their high-quality code implementation, which facilitates subsequent research.

Requirements

To run our code, please install all the dependency packages by using the following command:

pip install -r requirements.txt

NOTE: Different versions of packages (like pytorch, transformers, etc.) may lead to different results from the paper. However, the trend should still hold no matter what versions of packages you use.

Data

We pack the dataset (Multilingual Amazon Reviews Corpus) in the /data/ folder, which contains the link to original dataset (/data/original/) and the part sampled by us (/data/k-shot/).

NOTE: If you need to use their dataset, please cite their paper.

@inproceedings{keung2020multilingual,
  title={The Multilingual Amazon Reviews Corpus},
  author={Keung, Phillip and Lu, Yichao and Szarvas, Gy{\"o}rgy and Smith, Noah A},
  booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  pages={4563--4568},
  year={2020}
}

Run Our Code

We provide scripts for running experiments, see main_exp.sh.

Citation

Please cite our paper if you use UniPrompt in your work:

@article{huang2022zero,
  title={Zero-shot Cross-lingual Transfer of Prompt-based Tuning with a Unified Multilingual Prompt},
  author={Huang, Lianzhe and Ma, Shuming and Zhang, Dongdong and Wei, Furu and Wang, Houfeng},
  journal={arXiv preprint arXiv:2202.11451},
  year={2022}
}