Unsupervised multilingual grammar induction

• Main Authors: Snyder, Benjamin (Contributor), Naseem, Tahira (Contributor), Barzilay, Regina (Contributor)
• Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory (Contributor), Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor)
• Format: Article
• Language: English
• Published: Association for Computational Linguistics, 2010-10-14T12:48:54Z.
• Subjects: Article
• Online Access: Get fulltext

We investigate the task of unsupervised constituency parsing from bilingual parallel corpora. Our goal is to use bilingual cues to learn improved parsing models for each language and to evaluate these models on held-out monolingual test data. We formulate a generative Bayesian model which seeks to explain the observed parallel data through a combination of bilingual and monolingual parameters. To this end, we adapt a formalism known as unordered tree alignment to our probabilistic setting. Using this formalism, our model loosely binds parallel trees while allowing language-specific syntactic structure. We perform inference under this model using Markov Chain Monte Carlo and dynamic programming. Applying this model to three parallel corpora (Korean-English, Urdu-English, and Chinese-English) we find substantial performance gains over the CCM model, a strong monolingual baseline. On average, across a variety of testing scenarios, our model achieves an 8.8 absolute gain in F-measure.
National Science Foundation (U.S.) (grant IIS-0448168)
National Science Foundation (U.S.) (grant IIS-0835445)
National Science Foundation (U.S.) (grant IIS-0835652)