This model is a fine-tuned version of xlm-roberta-base for cross-domain term extraction tasks.
Model description
This model is a fine-tuned version of xlm-roberta-base for token classification, specifically designed to identify and classify terms within text sequences. The model assigns labels such as B-Term, I-Term, BN-Term, IN-Term, and O to individual tokens, allowing for the extraction of meaningful terms from the text.
Intended uses & limitations
The model is intended for term extraction tasks. It can be applied in domains like:
Named Entity Recognition (NER)
Information Extraction
Training and evaluation data
We fine-tuned the ACTER dataset where Named Entities are excluded from the gold standard. We trained on the Corruption and Wind Energy domain of all three languages (English, French, Dutch), and the Slovenian RSDO5 corpus, validated on the Equitation domain and tested on the Heart Failure domain.
Training procedure
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
We evaluate the performance of the ATE systems by comparing the candidate list extracted from the test set with the manually annotated gold standard term list for that specific test set. We use exact string matching to compare the retrieved terms to the ones in the gold standard and calculate Precision (P), Recall (R), and F1-score (F1).
The results are reported in Can cross-domain term extraction benefit from cross-lingual transfer and nested term labeling?.
Citation
If you use this model in your research or application, please cite it as follows:
@inproceedings{tran2022can,
title={Can cross-domain term extraction benefit from cross-lingual transfer?},
author={Tran, Hanh Thi Hong and Martinc, Matej and Doucet, Antoine and Pollak, Senja},
booktitle={International Conference on Discovery Science},
pages={363--378},
year={2022},
organization={Springer}
}
