Model Transfer for Tagging Low-resource Languages using a Bilingual Dictionary Model Transfer for Tagging Low-resource Languages using a Bilingual Dictionary
Paper summary They get multilingual alignments from dictionaries, then train a Bilstm pos tagger in source language, then automatically tag many tokens in the target language, then manually annotate 1000 tokens in target language, then train a system with combined loss over distant tagging and gold tagging. They add an additional output layer that is learned for the gold annotations.
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Model Transfer for Tagging Low-resource Languages using a Bilingual Dictionary
Fang, Meng and Cohn, Trevor
Association for Computational Linguistics - 2017 via Bibsonomy
Keywords: dblp


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