Posterior Regularization for Structured Latent Varaible Models

We present posterior regularization, a probabilistic framework for structured, weakly supervised learning. Our framework efficiently incorporates indirect supervision via constraints on posterior distributions of probabilistic models with latent variables. Posterior regularization separates model complexity from the complexity of structural constraints it is desired to satisfy. By directly imposing decomposable regularization on the posterior moments of latent variables during learning, we retain the computational efficiency of the unconstrained model while ensuring desired constraints hold in expectation. We present an efficient algorithm for learning with posterior regularization and illustrate its versatility on a diverse set of structural constraints such as bijectivity, symmetry and group sparsity in several large scale experiments, including multi-view learning, cross-lingual dependency grammar induction, unsupervised part-of-speech induction, and bitext word alignment. Disciplines Computer Sciences Comments Posterior Regularization for Structured Latent Variable Models , K. Ganchev, J. Graca, J. Gillenwater and B. Taskar, Journal of Machine Learning Research (JMLR), July 2010. Copyright held by the authors. This journal article is available at ScholarlyCommons: https://repository.upenn. edu/cis_papers/538 Journal of Machine Learning Research 10 (2009) ? Submitted ? ; Published ? Posterior Regularization for Structured Latent Variable Models Kuzman Ganchev KUZMAN@CIS.UPENN. EDU Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA João Graça JOAO.GRACA@L2F.INESC-ID.PT L2F Inesc-ID, Lisboa, Portugal Jennifer Gillenwater JENGI@CIS.UPENN. EDU Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA Ben Taskar TASKAR@CIS.UPENN. EDU Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA Editor: Lawrence Saul Abstract We present posterior regularization, a probabilistic framework for structured, weakly supervised learning. Our framework efficiently incorporates indirect supervision via constraints on posterior distributions of probabilistic models with latent variables. Posterior regularization separates model complexity from the complexity of structural constraints it is desired to satisfy. By directly imposing decomposable regularization on the posterior moments of latent variables during learning, we retain the computational efficiency of the unconstrained model while ensuring desired constraints hold in expectation. We present an efficient algorithm for learning with posterior regularization and illustrate its versatility on a diverse set of structural constraints such as bijectivity, symmetry and group sparsity in several large scale experiments, including multi-view learning, cross-lingual dependency grammar induction, unsupervised part-of-speech induction, and bitext word alignment. 1We present posterior regularization, a probabilistic framework for structured, weakly supervised learning. Our framework efficiently incorporates indirect supervision via constraints on posterior distributions of probabilistic models with latent variables. Posterior regularization separates model complexity from the complexity of structural constraints it is desired to satisfy. By directly imposing decomposable regularization on the posterior moments of latent variables during learning, we retain the computational efficiency of the unconstrained model while ensuring desired constraints hold in expectation. We present an efficient algorithm for learning with posterior regularization and illustrate its versatility on a diverse set of structural constraints such as bijectivity, symmetry and group sparsity in several large scale experiments, including multi-view learning, cross-lingual dependency grammar induction, unsupervised part-of-speech induction, and bitext word alignment. 1