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- W2251227481 abstract "We present a simple method for learning part-of-speech taggers for languages like Akawaio, Aukan, or Cakchiquel – languages for which nothing but a translation of parts of the Bible exists. By aggregating over the tags from a few annotated languages and spreading them via wordalignment on the verses, we learn POS taggers for 100 languages, using the languages to bootstrap each other. We evaluate our cross-lingual models on the 25 languages where test sets exist, as well as on another 10 for which we have tag dictionaries. Our approach performs much better (20-30%) than state-of-the-art unsupervised POS taggers induced from Bible translations, and is often competitive with weakly supervised approaches that assume high-quality parallel corpora, representative monolingual corpora with perfect tokenization, and/or tag dictionaries. We make models for all 100 languages available." @default.
- W2251227481 created "2016-06-24" @default.
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- W2251227481 date "2015-01-01" @default.
- W2251227481 modified "2023-10-18" @default.
- W2251227481 title "If all you have is a bit of the Bible: Learning POS taggers for truly low-resource languages" @default.
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- W2251227481 doi "https://doi.org/10.3115/v1/p15-2044" @default.
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