On Training Bi-directional Neural Network Language Model with Noise   Contrastive Estimation
  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. 
Tianxing He, Yu Zhang, Jasha Droppo, Kai Yu
cs.CL