The examples correspond to the experiments performed in our Findings of the EMNLP 2023 paper: T-Projection: High Quality Annotation Projection for Sequence Labeling Tasks.
You can get all the required datasets by running the download_datasets.py script.
Please, check the datasets/README.md file for more information about the datasets and the corresponding citations.
cd datasets/
python3 download_datasets.pyWe provide the following examples:
- Test_t-projection.sh: A small test to check that everything is working properly. This test does not use optimal hyperparameters.
- named_entity_recognition_Europarl.sh: Annotation projection from English to Spanish, German and Italian. We use the CoNLL03 data for training.
- named_entity_recognition_MasakhaNER.sh: Annotation projection from English into low-resource African languages. We use the CoNLL03 data for training.
- opinion_target_extraction_ABSA.sh: Annotation projection from English to Spanish, French, Russian and Turkish. We use the SemEval 2016 data for training.
- argument_mining_abstRCT.sh: Annotation projection from English to Spanish. We use the AbstRCT Neoplasm data for training.
