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.
More recently, REF integrated a large-scale neural language model into a machine translation system both by reranking N-best lists and by direct integration into the decoder.
Decoding with Large-Scale Neural Language Models Improves Translation
EMNLP
Computer Science