Decoding with Large Scale Neural Language Models Improves Translation
We explore the application of neural language models to machine translation. We develop a new model that combines the neural probabilistic language model of Bengio et al. rectified linear units, and noise contrastive estimation, and we incorporate it into a machine translation system both by reranking k best lists and by direct integration into the decoder. Our large scale, large vocabulary experiments across four language pairs show that our neural language model improves translation quality by up to 1.1 Bleu.
EMNLP