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Cross lingual projection of linguistic annotation suffers from many sourcesof bias and noise, leading to unreliable annotations that cannot be useddirectly. In this paper, we introduce a novel approach to sequence tagging thatlearns to correct the errors from cross-lingual projection using an explicitdebiasing layer. This is framed as joint learning over two corpora, one taggedwith gold standard and the other with projected tags. We evaluated with only1,000 tokens tagged with gold standard tags, along with more plentiful paralleldata. Our system equals or exceeds the state-of-the-art on eight simulatedlow-resource settings, as well as two real low-resource languages, Malagasy andKinyarwanda.

Learning when to trust distant supervision: An application to low-resource POS tagging using cross-lingual projection