Papers with French text
Domain Mismatch Doesn’t Always Prevent Cross-lingual Transfer Learning (2022.lrec-1)
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| Challenge: | Recent studies have reported that domain mismatch prevents cross-lingual transfer . UBLI and UNMT do not work well when underlying monolingual corpora come from different domains . |
| Approach: | They show that a simple initialization regimen can overcome domain mismatch in cross-lingual transfer . they pre-train word embeddings on concatenated domain-mismatched corpora and use them as initializations . |
| Outcome: | The initialization regimen can overcome the domain mismatch effect in cross-lingual transfer learning . the initializations were used for MUSE UBLI, UN Parallel UNMT, and the SemEval 2017 task . |
On the importance of pre-training data volume for compact language models (2020.emnlp-main)
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| Challenge: | Recent advances in language modeling have led to computationally intensive and resource-demanding state-of-the-art models. |
| Approach: | They investigate the impact of pre-training data volume on compact language models . they use a French question answering task to train models with as little as 100 MB of text . |
| Outcome: | The results show that pre-training data volume can improve models with as little as 100 MB of text . the results suggest that the model performance is poorer with less data than with larger datasets . |