For example, POS tags can be projected via word alignments, and the projected POS is then used to train a model in the lowresource language Zhang et al., 2016;<cite> Fang and Cohn, 2016)</cite> . These methods overall have limited effectiveness due to errors in the alignment and fundamental differences between the languages. They also assume a large parallel corpus, which may not be available for many low-resource languages. To address these limitations, we propose a new technique for low resource tagging, with more modest resource requirements: 1) a bilingual dictionary; 2) monolingual corpora in the high and low resource languages; and 3) a small annotated corpus of around 1, 000 tokens in the low-resource language.