Shared Context Probabilistic Transducers

Recently, a model for supervised learning of probabilistic transduc(cid: 173) ers represented by suffix trees was introduced. However, this algo(cid: 173) rithm tends to build very large trees, requiring very large amounts of computer memory. In this paper, we propose anew, more com(cid: 173) pact, transducer model in which one shares the parameters of distri(cid: 173) butions associated to contexts yielding similar conditional output distributions. We illustrate the advantages of the proposed algo(cid: 173) rithm with comparative experiments on inducing a noun phrase recogmzer.

Authors

• Yoshua Bengio Mila University of Montreal
• Samy Bengio
• Jean-Franc Isabelle
• Yoram Singer

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