Abstract

We describe how to effectively train neural network
based language models on large data sets. Fast convergence
during training and better overall performance is observed when
the training data are sorted by their relevance. We introduce
hash-based implementation of a maximum entropy model, that
can be trained as a part of the neural network model. This
leads to significant reduction of computational complexity. We
achieved around 10% relative reduction of word error rate on
English Broadcast News speech recognition task, against large
4-gram model trained on 400M tokens.

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