Don’t Use English Dev: On the Zero-Shot Cross-Lingual Evaluation of Contextual Embeddings (2020.emnlp-main)
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| Challenge: | Multilingual contextual embeddings have demonstrated state-of-the-art performance in zero-shot cross-lingual transfer learning. |
| Approach: | They show that English dev accuracy makes it difficult to obtain reproducible results . they recommend providing oracle scores alongside zero-shot results if possible . |
| Outcome: | mBERT and XLM have shown strong performance on cross-lingual recognition, text classification, dependency parsing, and other tasks. |
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| Challenge: | Existing studies have focused on zero-shot cross-lingual transfer . mBERT, mBART and mT5 provide high-quality representations for texts in various languages . |
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Why Does Zero-Shot Cross-Lingual Generation Fail? An Explanation and a Solution (2023.findings-acl)
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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: | Pretrained contextual representation models have pushed forward the state-of-the-art on many NLP tasks. |
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| Challenge: | Existing work on cross-language entity linking grounds mentions written in multiple languages to a monolingual knowledge base is lacking. |
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| Challenge: | Pre-trained multilingual language encoders do not precisely align words and phrases across languages. |
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| Challenge: | Existing studies have shown that deep, contextualized language models can encode syntactic and named entity information, but they have focused on what models trained on English capture about English. |
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Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages (2022.acl-long)
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| Challenge: | Existing studies on cross-lingual generalisability of large pre-trained models use English training data and test data in unseen languages. |
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