Unsupervised Multilingual Learning for POS Tagging

We demonstrate the effectiveness of multilingual learning for unsupervised part-of-speech tagging. The key hypothesis of multilingual learning is that by combining cues from multiple languages, the structure of each becomes more apparent. We formulate a hierarchical Bayesian model for jointly predicting bilingual streams of part-of-speech tags. The model learns language-specific features while capturing cross-lingual patterns in tag distribution for aligned words. Once the parameters of our model have been learned on bilingual parallel data, we evaluate its performance on a held-out monolingual test set. Our evaluation on six pairs of languages shows consistent and significant performance gains over a state-of-the-art monolingual baseline. For one language pair, we observe a relative reduction in error of 53%.

• Type: Conference
• Year: 2008
• Where: EMNLP
• Authors: Benjamin Snyder, Tahira Naseem, Jacob Eisenstein, Regina Barzilay
• Keywords: EMNLP 2008, Hierarchical Bayesian Model, Multilingual Learning, Natural Language Processing, Unsupervised Part-of-speech