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- W4286974328 abstract "Recent studies in zero-shot cross-lingual learning using multilingual models have falsified the previous hypothesis that shared vocabulary and joint pre-training are the keys to cross-lingual generalization. Inspired by this advancement, we introduce a cross-lingual transfer method for monolingual models based on domain adaptation. We study the effects of such transfer from four different languages to English. Our experimental results on GLUE show that the transferred models outperform the native English model independently of the source language. After probing the English linguistic knowledge encoded in the representations before and after transfer, we find that semantic information is retained from the source language, while syntactic information is learned during transfer. Additionally, the results of evaluating the transferred models in source language tasks reveal that their performance in the source domain deteriorates after transfer." @default.
- W4286974328 created "2022-07-25" @default.
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- W4286974328 date "2021-09-15" @default.
- W4286974328 modified "2023-09-25" @default.
- W4286974328 title "Cross-lingual Transfer of Monolingual Models" @default.
- W4286974328 doi "https://doi.org/10.48550/arxiv.2109.07348" @default.
- W4286974328 hasPublicationYear "2021" @default.
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