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Inducing Synchronous Grammars with Slice Sampling AbstractThis paper describes an efficient sampler for synchronous grammar induction under a non- parametric Bayesian prior. Inspired by ideas from slice sampling, our sampler is able to draw samples from the posterior distributions of models for which the standard dynamic pro- graming based sampler proves intractable on non-trivial corpora. We compare our sampler to a previously proposed Gibbs sampler and demonstrate strong improvements in terms of both training log-likelihood and performance on an end-to-end translation evaluation.
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