All datasets / ipipan
ipipan/silesian-wikipedia-clean-20230901
The declared licence (cc-by-sa-4.0) is classified as share-alike.
- Declared licence
- cc-by-sa-4.0, from the tagEvidence: README.md at 5abdec1
- Licence files
- No licence file at the top level of the repositoryEvidence: file list at 5abdec1
- Declared sources
- None listed in the card metadata
- Access gated on Hugging Face
- No
Citation text from the card
@inproceedings{rybak-2024-transferring-bert,
title = "Transferring {BERT} Capabilities from High-Resource to Low-Resource Languages Using Vocabulary Matching",
author = "Rybak, Piotr",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.1456",
pages = "16745--16750",
abstract = "Pre-trained language models have revolutionized the natural language understanding landscape, most notably BERT (Bidirectional Encoder Representations from Transformers). However, a significant challenge remains for low-resource languages, where limited data hinders the effective training of such models. This work presents a novel approach to bridge this gap by transferring BERT capabilities from high-resource to low-resource languages using vocabulary matching. We conduct experiments on the Silesian and Kashubian languages and demonstrate the effectiveness of our approach to improve the performance of BERT models even when the target language has minimal training data. Our results highlight the potential of the proposed technique to effectively train BERT models for
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dataset_licence({ "id": "ipipan/silesian-wikipedia-clean-20230901" })
signal: "permissive"
stated_licence: "cc-by-sa-4.0"
sources: 0