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On Training Bi-directional Neural Network Language Model with Noise Contrastive Estimation

2016-02-19Code Available0· sign in to hype

Tianxing He, Yu Zhang, Jasha Droppo, Kai Yu

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Abstract

We propose to train bi-directional neural network language model(NNLM) with noise contrastive estimation(NCE). Experiments are conducted on a rescore task on the PTB data set. It is shown that NCE-trained bi-directional NNLM outperformed the one trained by conventional maximum likelihood training. But still(regretfully), it did not out-perform the baseline uni-directional NNLM.

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