This is the official repository of FILTER.

We provide Docker image for easier reproduction. Please use dockers/Dockerfile or pull image directly.

docker pull studyfang/multilingual:xtreme

To run docker without sudo permission, please refer this documentation Manage Docker as a non-root user. Then, you could start docker, e.g.

docker run --gpus all -it -v /path/to/FILTER:/ssd -it studyfang/multilingual:xtreme bash

NOTE: Please make sure you have set up the environment correctly.

1. Download data and our models

Please set your DATA_ROOT in init.sh, and then run the following command to download specified task and its pretrained FILTER models.

bash scripts/download_data.sh ${task}

To download all tasks and its pretrained models, please run bash scripts/download_data.sh which may take a while.

2. Evaluate our pretrained models which are save in $DATA_ROOT/outputs/phase${idx}/${task} :

bash eval.sh -t ${task} -n phase${idx}/${task}

where

• idx could be 1 (without self-teaching) or 2(+ self-teaching).
• task is the name of the task to evaluate from ([xnli, pawsx, mlqa, tydiqa, xquad, udpos, panx])

For QA model training, we use translated training data from XTREME team. Please refere to their repo or their translation directly. Once your data is ready, simply run the following command to train a FILTER model for supported XTREME tasks:

bash train.sh -t ${task} -n ${task}

To use different number of local and fusion layers, you can run this command:

bash train.sh -t ${task} -n ${task}_k${k}_m${m} -x "--filter_k ${k} --filter_m ${m}"

where

• task is the name of the task to train from ([xnli, pawsx, mlqa, tydiqa, xquad, udpos, panx])
• k is the number of fusion layers
• m is the number of local layers

The output model will be save into ${DATA_ROOT}/outputs/${task}_k${k}_m${m}.

Note that we ran experiments on 8 V100 GPUs for FILTER models. You may need to increase gradient_accumulation_steps if you have less GPUs.

If you use this code useful, please star our repo or consider citing:

@article{fang2020filter,
  title={FILTER: An enhanced fusion method for cross-lingual language understanding},
  author={Fang, Yuwei and Wang, Shuohang and Gan, Zhe and Sun, Siqi and Liu, Jingjing},
  journal={arXiv preprint arXiv:2009.05166},
  year={2020}
}

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.

When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repositories using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

MIT