The KIT-LIMSI Translation System for WMT 2015

This paper presented the joined submission of KIT and LIMSI to the English to German translation task of WMT 2015. In this year submission, we integrated a neural network-based translation model into a phrase-based translation model by rescoring the n-best lists. Since the computation complexity is one of the main issues for continuous space models, we compared two techniques to reduce the computation cost. We investigated models using a structured output layer as well as models trained with noise contrastive estimation. Furthermore, we evaluated a new method to obtain the best log-linear combination in the rescoring phase.
Using these techniques, we were able to improve the BLEU score of the baseline phrase-based system by 1.4 BLEU points.