This repository contains the code for the ACL 2023 paper "Improving Self-training for Cross-lingual Named Entity Recognition with Contrastive and Prototype Learning"
- torch>=1.6
- pytorch-lightning>=0.9.0
- tokenizers
- transformers
To install the dependencies, run:
pip install -r requirements.txt
Our experiments are run on a single Nvidia V100 32GB GPU.
Before running the experiments, you will need to convert the NER dataset from CoNLL format to span-based format by running the following scripts:
cd data
bash data_preprocess.sh
We have provided the processed German dataset from CoNLL as an example in data/conll03_de.
To generate the pseudo-labeled data, run the following script:
bash run_generate_pseudo.sh de conll03
For example, the generated German pseudo labels will be stored in train_logs/conll03/genpseudo_de_0/.
To start training of ContProto, run the following script:
bash run_contpro_auto-margin.sh de conll03
If you find this repository useful, please cite our paper:
@inproceedings{zhou-etal-2023-improving,
title = "Improving Self-training for Cross-lingual Named Entity Recognition with Contrastive and Prototype Learning",
author = "Zhou, Ran and
Li, Xin and
Bing, Lidong and
Cambria, Erik and
Miao, Chunyan",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
year = "2023",
}