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A new whole-sentence language model - neural trans-dimensional random fieldlanguage model (neural TRF LM), where sentences are modeled as a collection ofrandom fields, and the potential function is defined by a neural network, hasbeen introduced and successfully trained by noise-contrastive estimation (NCE).In this paper, we extend NCE and propose dynamic noise-contrastive estimation(DNCE) to solve the two problems observed in NCE training. First, a dynamicnoise distribution is introduced and trained simultaneously to converge to thedata distribution. This helps to significantly cut down the noise sample numberused in NCE and reduce the training cost. Second, DNCE discriminates betweensentences generated from the noise distribution and sentences generated fromthe interpolation of the data distribution and the noise distribution. Thisalleviates the overfitting problem caused by the sparseness of the trainingset. With DNCE, we can successfully and efficiently train neural TRF LMs onlarge corpus (about 0.8 billion words) with large vocabulary (about 568 Kwords). Neural TRF LMs perform as good as LSTM LMs with less parameters andbeing 5x~114x faster in rescoring sentences. Interpolating neural TRF LMs withLSTM LMs and n-gram LMs can further reduce the error rates.

Improved training of neural trans-dimensional random field language models with dynamic noise-contrastive estimation