README.md

UniBridge: A Unified Approach to Cross-Lingual Transfer Learning for Low-Resource Languages

In this paper, we introduce UniBridge (Cross-Lingual Transfer Learning with Optimized Embeddings and Vocabulary), a comprehensive approach developed to improve the effectiveness of Cross-Lingual Transfer Learning, particularly in languages with limited resources. Our approach tackles two essential elements of a language model: the initialization of embeddings and the optimal vocabulary size. Specifically, we propose a novel embedding initialization method that leverages both lexical and semantic alignment for a language. In addition, we present a method for systematically searching for the optimal vocabulary size, ensuring a balance between model complexity and linguistic coverage. Our experiments across multilingual datasets show that our approach greatly improves the F1-Score in several languages. UniBridge is a robust and adaptable solution for cross-lingual systems in various languages, highlighting the significance of initializing embeddings and choosing the right vocabulary size in cross-lingual environments.

Details of the model architecture and experimental results can be found in our following paper.

Please CITE our paper whenever this repository is used to help produce published results or incorporated into other software.

@inproceedings{UniBridge,
    title = {{UniBridge: A Unified Approach to Cross-Lingual Transfer Learning for Low-Resource Languages}},
    author = {Pham, Trinh and Le, Khoi M. and Luu, Anh Tuan},
    year = {2024},
    booktitle = {Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
}

Training

1. Building tokenizer.

1.1. Install dependencies

conda create -n unibridge.tokenizer python=3.9
conda activate unibridge.tokenizer
pip install -r mbert/requirements-main.txt

1.2. Building tokenizer

cd mbert
sh build.sh
cd ../xlm
sh build.sh

2. Training

2.1. Install dependencies

conda create -n unibridge.adapter python=3.9
conda activate unibridge.adapter
pip install -r mbert/requirements-adapter.txt

2.2. Training

cd mbert
sh train.sh
cd ../xlm
sh train.sh

Evaluation

1. Determine harmony weight

cd mbert
python weight.py
cd ../xlm
python weight.py

2. Evaluation

cd mbert
python pinference_ner.py
python pinference_nli.py
python pinference_pos.py

cd ../xlm
python pinference_ner.py
python pinference_nli.py
python pinference_pos.py