LuxemBERT: Simple and Practical Data Augmentation in Language Model Pre-Training for Luxembourgish (2022.lrec-1)
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Cedric Lothritz, Bertrand Lebichot, Kevin Allix, Lisa Veiber, Tegawende Bissyande, Jacques Klein, Andrey Boytsov, Clément Lefebvre, Anne Goujon
| Challenge: | Pre-trained Language Models such as BERT are ubiquitous in NLP but are scarce for low-resource languages such as Luxembourgish. |
| Approach: | They propose a BERT model for Luxembourgish language that they use to augment pre-training datasets by partially translating text data from a closely related language. |
| Outcome: | The proposed model outperforms the baseline model and the mBERT model in Luxembourgish. |
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