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We explore cross-lingual transfer of register classification for webdocuments. Registers, that is, text varieties such as blogs or news are one ofthe primary predictors of linguistic variation and thus affect the automaticprocessing of language. We introduce two new register annotated corpora,FreCORE and SweCORE, for French and Swedish. We demonstrate that deeppre-trained language models perform strongly in these languages and outperformprevious state-of-the-art in English and Finnish. Specifically, we show 1) thatzero-shot cross-lingual transfer from the large English CORE corpus can matchor surpass previously published monolingual models, and 2) that lightweightmonolingual classification requiring very little training data can reach orsurpass our zero-shot performance. We further analyse classification resultsfinding that certain registers continue to pose challenges in particular forcross-lingual transfer.

Beyond the English Web: Zero-Shot Cross-Lingual and Lightweight Monolingual Classification of Registers