Most semi-supervised methods in Natural Language Processing capitalize on unannotated resources in a single language; however, information can be gained from using parallel resources in more than one language, since translations of the same utterance in different languages can help to disambiguate each other. We demonstrate a method that makes effective use of vast amounts of bilingual text (a.k.a. bitext) to improve monolingual systems. We propose a factored probabilistic sequence model that encourages both crosslanguage and intra-document consistency. A simple Gibbs sampling algorithm is introduced for performing approximate inference. Experiments on English-Chinese Named Entity Recognition (NER) using the OntoNotes dataset demonstrate that our method is significantly more accurate than state-ofthe- art monolingual CRF models in a bilingual test setting. Our model also improves on previous work by Burkett et al. (2010), achieving a relative error reduction of 10.8% and 4.5% in Chinese and English, respectively. Furthermore, by annotating a moderate amount of unlabeled bi-text with our bilingual model, and using the tagged data for uptraining, we achieve a 9.2% error reduction in Chinese over the state-ofthe- art Stanford monolingual NER system.