Jean-Franc Isabelle

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Recently a model for supervised learning of probabilistic transduc ers represented by su x trees was introduced However this algo rithm tends to build very large trees requiring very large amounts of computer memory In this paper we propose a new more com pact transducer model in which one shares the parameters of distri butions associated to contexts(More)
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