Syntax-augmented Multilingual BERT

Official code release of our ACL 2021 work, Syntax-augmented Multilingual BERT for Cross-lingual Transfer.

[Notes]

This repository provides implementations for three NLP applications.
- Text classification, named entity recognition, and task-oriented semantic parsing.
We will release the question answering (QA) model implementation soon.

Setup

We setup a conda environment in order to run experiments. We assume anaconda and Python 3.6 is installed. The additional requirements (as noted in requirements.txt can be installed by running the following script:

bash install_tools.sh

Data Preparation

The next step is to download the data. To this end, first create a download folder with mkdir -p download in the root of this project. You then need to manually download panx_dataset (for NER) from here (note that it will download as AmazonPhotos.zip) to the download directory. Finally, run the following command to download the remaining datasets:

bash scripts/download_data.sh

To get the POS-tags and dependency parse of input sentences, we use UDPipe. Go to the udpipe directory and run the task-specific scripts - [xnli.sh|pawsx.sh|panx.sh|mtop.sh].

Training and Evaluation

The evaluation results (on the test set) are saved in ${SAVE_DIR} directory (check the bash scripts).

Text Classification

cd scripts
bash xlt_classify.sh GPU TASK USE_SYNTAX SEED

For cross-lingual text classification, do the following.

# for XNLI
bash xlt_classify.sh 0 xnli false 1111

# for PAWS-X
bash xlt_classify.sh 0 pawsx false 1111

For syntax-agumented MBERT experiments, set USE_SYNTAX=true.
For generalized cross-lingual text classification evaluation, use the gxlt_classify.sh script.

Named Entity Recognition

cd scripts
bash panx.sh GPU USE_SYNTAX SEED

For syntax-agumented MBERT experiments, set USE_SYNTAX=true.
For the CoNLL NER datasets, same set of scripts can be used (with revision).

Task-oriented Semantic Parsing

cd scripts
bash mtop.sh GPU USE_SYNTAX SEED

For syntax-agumented MBERT experiments, set USE_SYNTAX=true.
Since, mATIS++ dataset is not publicly available, we do not release the scripts.

Acknowledgement

We acknowledge the efforts of the authors of the following repositories.

Citation

@inproceedings{ahmad-etal-2021-syntax,
    title = "Syntax-augmented Multilingual {BERT} for Cross-lingual Transfer",
    author = "Ahmad, Wasi  and
      Li, Haoran  and
      Chang, Kai-Wei  and
      Mehdad, Yashar",
    booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.acl-long.350",
    pages = "4538--4554",
}

wasiahmad/Syntax-MBERT