| Challenge: | a pre-trained language model with low OOV can improve performance for transfer learning . a vocabulary surrogate can provide performance boosts with no additional computation cost . |
| Approach: | They propose multiple methods to mitigate OOV during downstream task fine-tuning . they demonstrate that vocabulary surrogates can provide performance boosts with no additional computation cost . |
| Outcome: | The proposed methods improve performance with the same parameter count when combined with fine-tuning. |
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| Challenge: | Multilingual pretrained language models have shown impressive results for cross-lingual transfer, but due to the constant model capacity, multilingual pre-training usually lags behind the monolingual competitors. |
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UNKs Everywhere: Adapting Multilingual Language Models to New Scripts (2021.emnlp-main)
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An Empirical Investigation Towards Efficient Multi-Domain Language Model Pre-training (2020.emnlp-main)
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