No abstract

The log-linear combination of different features is an important component of SMT systems. It allows for the easy integartion of models into the system and is used during decoding as well as for nbest list rescoring. With the recent success of more complex models like neural network-based translation models, n-best list rescoring attracts again more attention. In this work, we present a new technique to train the log-linear model based on the ListNet algorithm. This technique scales to many features, considers the whole list and not single entries during learning and can also be applied to more complex models than a log-linear combination. Using the new learning approach, we improve the translation quality of a largescale system by 0.8 BLEU points during rescoring and generate translations which are up to 0.3 BLEU points better than other learning techniques such as MERT or MIRA.

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.

Continuous-space translation models have recently emerged as extremely powerful ways to boost the performance of existing translation systems. A simple, yet effective way to integrate such models in inference is to use them in an N -best rescoring step. In this paper, we focus on this scenario and show that the performance gains in rescoring can be greatly increased when the neural network is trained jointly with all the other model parameters, using an appropriate objective function. Our approach is validated on two domains, where it outperforms strong baselines.

This paper describes LIMSI's submissions to the shared WMT'15 translation task. We report results for French-English, Russian-English in both directions, as well as for Finnish-into-English. Our submissions use NCODE and MOSES along with continuous space translation models in a post-processing step. The main novelties of this year's participation are the following: for Russian-English, we investigate a tailored normalization of Russian to translate into English, and a two-step process to translate first into simplified Russian, followed by a conversion into inflected Russian. For French-English, the challenge is domain adaptation, for which only monolingual corpora are available. Finally, for the Finnish-to-English task, we explore unsupervised morphological segmentation to reduce the sparsity of data induced by the rich morphology on the Finnish side.