Papers by Lisa Veiber
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. |
Evaluating Pretrained Transformer-based Models on the Task of Fine-Grained Named Entity Recognition (2020.coling-main)
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| Challenge: | Named Entity Recognition (NER) is a fundamental task in Natural Language Processing (NLP). |
| Approach: | They compare three transformer-based names to two non-transformer-based ones . they find transformer-derived models incrementally outperform non-tranformer models . |
| Outcome: | The proposed models outperform the studied models in most domains with respect to the F1 score. |