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New directions in connectionist language modeling
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Pattern Recognition
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Connectionist language models offer many advantages over their statistical counterparts, but they also have some drawbacks like a much more expensive computational cost. This paper describes a novel method to overcome this problem. A set of normalization values associated to the most frequent n -gramsis pre-computed and the model is smoothed with lower n -gramconnectionist or statistical models. The proposed approach is favourably compared to standard connectionist language models and with statistical back-off language models.