The KIT-LIMSI Translation System for WMT 2015

By Quoc-Khanh Do, Teresa Herrmann, Jan Niehues, Alexander Allauzen, François Yvon, A. Waibel. 2015

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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.

Subfields: Artificial Intelligence