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

Meng Fang, Trevor Cohn
2016 Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning  
Cross lingual projection of linguistic annotation suffers from many sources of bias and noise, leading to unreliable annotations that cannot be used directly. In this paper, we introduce a novel approach to sequence tagging that learns to correct the errors from cross-lingual projection using an explicit debiasing layer. This is framed as joint learning over two corpora, one tagged with gold standard and the other with projected tags. We evaluated with only 1,000 tokens tagged with gold
more » ... tags, along with more plentiful parallel data. Our system equals or exceeds the state-of-the-art on eight simulated lowresource settings, as well as two real lowresource languages, Malagasy and Kinyarwanda.
doi:10.18653/v1/k16-1018 dblp:conf/conll/FangC16 fatcat:tmsnbysb2zej5fo22idvi4pmoa