Learning when to trust distant supervision: An application to low-resource POS tagging using cross-lingual projection
This addresses the challenge of obtaining accurate linguistic annotations for low-resource languages, which is incremental as it builds on existing cross-lingual projection methods.
The paper tackles the problem of unreliable annotations from cross-lingual projection for low-resource POS tagging by introducing a debiasing layer that corrects errors through joint learning, achieving state-of-the-art results on eight simulated settings and two real low-resource languages.
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 standard tags, along with more plentiful parallel data. Our system equals or exceeds the state-of-the-art on eight simulated low-resource settings, as well as two real low-resource languages, Malagasy and Kinyarwanda.