Code for IJCAI 2024 paper "Improving Pseudo Labels with Global-Local Denoising Framework for Cross-lingual Named Entity Recognition"

To install requirements:

pip install requirements.txt

• For convenience, it is recommended to use the example we gave to arrange the file directory

  data/

  ├── datasetName_languageName/

  │ ├── spanner.train

  │ ├── spanner.dev

  │ ├── spanner.unlabel

  │ └── spanner.test

• The data needs to follow the json format given in the file. Conll2003 data formats (as shown in the text file) can be processed using the bio2spannerformat.py
• spanner.train and spanner.dev are dataset from the source language, and spanner.unlabel and spanner.test are from the target language.

For more details, see the files.

We load the pre-trained model from a local file and have the PRETRAINED variable defined in the script file. Please handle it according to your own situation

# target_language_name; dataset_name; epochs
bash generate_pseudo_labels.sh demoLang conll03 8

# target_language_name; dataset_name; epochs; pseudo_labels_id(based on the epoch of generate_pseudo_labels.py)
bash train_tgt_model.sh demoLang conll03 5 7

Our code framework refers to ContProto, many thaks.

If you find this repository useful, please cite our paper. Thanks for your support.