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This paper presents two Markov chain Monte Carlo (MCMC) algorithms for Bayesian inference of probabilistic con- text free grammars (PCFGs) from ter- minal strings, providing an alternative to maximum-likelihood estimation …...

We examine the problem of overcoming noisy word-level alignments when learn- ing tree-to-string translation rules. Our approach introduces new rules, and re- estimates rule probabilities using EM. The major obstacles …...

A number of results in the study of real- time sentence comprehension have been explained by computational models as re- sulting from the rational use of probabilis- tic linguistic information. …...

We describe a Bayesian inference algorithm that can be used to train any cascade of weighted finite-state transducers on end-to- end data. We also investigate the problem of automatically selecting …...

We introduce a novel Bayesian approach for deciphering complex substitution ciphers. Our method uses a decipherment model which combines information from letter n-gram lan- guage models as well as word …...

In this paper, we propose a new Bayesian inference method to train statistical ma- chine translation systems using only non- parallel corpora. Following a probabilis- tic decipherment approach, we first …...

AbstractVisual Word Sense Disambiguation (VWSD) is a task to find the image that most accurately depicts the correct sense of the target word for the given context. Previously, image-text matching …...