nmT5 - Is parallel data still relevant for pre-training massively multilingual language models? (2021.acl-short)
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| Challenge: | Recent studies have shown that cross-lingual transfer learning in pre-trained multilingual models could be improved further by incorporating parallel data. |
| Approach: | They propose to integrate parallel data into mT5 pre-training to improve results on downstream multilingual and cross-lingual tasks. |
| Outcome: | The proposed model improves cross-lingual transfer significantly in small fine-tuning datasets and small model sizes. |
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