The KIT-LIMSI Translation System for WMT 2015
Thanh-Le Ha, Quoc Khanh, Eunah Cho, Jan Niehues, Alexandre Allauzen, François Yvon, Alex Waibel · 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.