Multimodal Pretraining Unmasked: A Meta-Analysis and a Unified Framework of Vision-and-Language BERTs (2021.tacl-1)
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| Challenge: | Large-scale pretraining and task-specific fine-tuning are now the standard methodology for many tasks in computer vision and natural language processing. |
| Approach: | They propose to combine two types of vision and language BERTs to create a theoretical framework that can be unified under different theoretical frameworks. |
| Outcome: | The proposed models can be classified into single-stream or dual-stream encoders and are unified under a single theoretical framework. |
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| Challenge: | Multilingual pretrained language models have demonstrated remarkable zero-shot cross-lingual transfer capabilities. |
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| Challenge: | Recent work has shown that multilingual pretraining works, but is unable to measure these effects. |
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| Challenge: | Masked language modeling is one of the most popular pretraining recipes in natural language processing. |
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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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| Challenge: | Various studies show that pretrained language models cannot replace encoders in neural machine translation despite their success in other tasks. |
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