  We demonstrate the effectiveness of multilingual learning for unsupervised
part-of-speech tagging. The central assumption of our work is that by combining
cues from multiple languages, the structure of each becomes more apparent. We
consider two ways of applying this intuition to the problem of unsupervised
part-of-speech tagging: a model that directly merges tag structures for a pair
of languages into a single sequence and a second model which instead
incorporates multilingual context using latent variables. Both approaches are
formulated as hierarchical Bayesian models, using Markov Chain Monte Carlo
sampling techniques for inference. Our results demonstrate that by
incorporating multilingual evidence we can achieve impressive performance gains
across a range of scenarios. We also found that performance improves steadily
as the number of available languages increases.
