Paper: Decoding with Large-Scale Neural Language Models Improves Translation

ACL ID D13-1140
Title Decoding with Large-Scale Neural Language Models Improves Translation
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2013
Authors

We explore the application of neural language models to machine translation. We develop a new model that combines the neural proba- bilistic language model of Bengio et al., rec- tified linear units, and noise-contrastive esti- mation, 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 neu- ral language model improves translation qual- ity by up to 1.1 Bleu.