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Even for common NLP tasks, sufficient supervision is not available in many languages-morphological tagging is no exception. In the work presented here, we explore a transfer learning scheme, whereby we train character-level recurrent neural taggers to predict morphological taggings for high-resource languages and low-resource languages together. Learning joint character representations among multiple related languages successfully enables knowledge transfer from the high-resource languages todoi:10.18653/v1/d17-1078 dblp:conf/emnlp/CotterellH17 fatcat:mlwjbvefsrckvb2eihoppwae6y