Cross-lingual, Character-Level Neural Morphological Tagging


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 to the low-resource ones.

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing