| Challenge: | Cananical Correlation Analysis (CCA) of the internal representations of a pre- trained, multilingual BERT model reveals that the model partitions representations for each language rather than using a common, shared, interlingual space. |
| Approach: | They propose to use a multilingual BERT model to partition representations for each language rather than using a common, shared, interlingual space. |
| Outcome: | The results show that the model partitions representations for each language rather than using a common, shared, interlingual space. |
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| Challenge: | Recent work has shown that multilingual pretraining works, but is unable to measure these effects. |
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How Multilingual is Multilingual BERT? (P19-1)
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| Challenge: | In addition, information on part-of-speech tagging is spread over different parts of the network and the pipeline might not be as neat as it seems. |
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Multilingual BERT has an accent: Evaluating English influences on fluency in multilingual models (2023.findings-eacl)
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| Challenge: | Multilingual pretrained language models have demonstrated remarkable zero-shot cross-lingual transfer capabilities. |
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On the Cross-lingual Transferability of Monolingual Representations (2020.acl-main)
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| Challenge: | State-of-the-art unsupervised multilingual models generalize in zero-shot cross-lingual setting . generalization ability attributed to shared subword vocabulary and joint training across multiple languages . |
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Extending Multilingual BERT to Low-Resource Languages (2020.findings-emnlp)
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| Challenge: | Multilingual BERT (M-BERT) has been a huge success in both supervised and zero-shot cross-lingual transfer learning. |
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