Jean-Franc Isabelle

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Recently, a model for supervised learning of probabilistic transducers represented by suux trees was introduced. However, this algorithm tends to build very large trees, requiring very large amounts of computer memory. In this paper, we propose a new, more compact , transducer model in which one shares the parameters of distributions associated to contexts(More)
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