| Challenge: | Existing studies on cross-lingual learning using multilingual models cast doubt on shared vocabulary and joint pre-training . et al. (2005) show that model knowledge learned in the source language enhances the learning of the target language independently of language proximity. |
| Approach: | They propose a method for transferring monolingual models to other languages through continuous pre-training and investigate their results in English. |
| Outcome: | The proposed method outperforms a model trained from scratch in the GLUE benchmark for English . it shows that model knowledge from the source language enhances the learning of syntactic and semantic knowledge in english. |
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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. |
| Approach: | They propose to transfer the knowledge from monolingual pretrained models to multilingual ones to improve zero-shot cross-lingual classification by using machine translation systems. |
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Emerging Cross-lingual Structure in Pretrained Language Models (2020.acl-main)
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| Challenge: | Recent work has shown that multilingual pretraining works, but is unable to measure these effects. |
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Analyzing the Mono- and Cross-Lingual Pretraining Dynamics of Multilingual Language Models (2022.emnlp-main)
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| Challenge: | Existing studies on multilingual models have focused on their cross-lingual transfer behavior . a recent study examined multilingual model learning from the multilingual pretraining signal . |
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Language Contamination Helps Explains the Cross-lingual Capabilities of English Pretrained Models (2022.emnlp-main)
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| Challenge: | a large number of pretraining corpora are not publicly available, and it is unclear how much foreign language data exists in monolingual models. |
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| Challenge: | Multilingual pretraining models can transfer knowledge to target languages with minimal or no examples . underlying mechanisms for this transfer remain unclear, with hypotheses ranging from language contamination to syntactic similarity. |
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Analyzing the Evaluation of Cross-Lingual Knowledge Transfer in Multilingual Language Models (2024.eacl-long)
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| Challenge: | Recent advances in training multilingual models on large datasets have shown promising results in knowledge transfer across languages. |
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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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Unknown Script: Impact of Script on Cross-Lingual Transfer (2024.naacl-srw)
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| Challenge: | Existing models for high-resource languages are not available for all languages, and the vast majority of the world's languages are excluded from these models. |
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Multi-Source Cross-Lingual Model Transfer: Learning What to Share (P19-1)
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On the Acquisition of Shared Grammatical Representations in Bilingual Language Models (2025.acl-long)
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| Challenge: | Crosslingual transfer is crucial to contemporary language models’ multilingual capabilities, but how it occurs is not well understood. |
| Approach: | They use structural priming to study grammatical representations in humans by controlling for training data quantity and language exposure. |
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