Carmen Heger


Spelling Correction of User Search Queries through Statistical Machine Translation
Saša Hasan | Carmen Heger | Saab Mansour
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing


Machine translation for global e-commerce on eBay
Jyoti Guha | Carmen Heger
Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Users Track


The RWTH Aachen Machine Translation System for WMT 2010
Carmen Heger | Joern Wuebker | Matthias Huck | Gregor Leusch | Saab Mansour | Daniel Stein | Hermann Ney
Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR

A combination of hierarchical systems with forced alignments from phrase-based systems
Carmen Heger | Joern Wuebker | David Vilar | Hermann Ney
Proceedings of the 7th International Workshop on Spoken Language Translation: Papers

Currently most state-of-the-art statistical machine translation systems present a mismatch between training and generation conditions. Word alignments are computed using the well known IBM models for single-word based translation. Afterwards phrases are extracted using extraction heuristics, unrelated to the stochastic models applied for finding the word alignment. In the last years, several research groups have tried to overcome this mismatch, but only with limited success. Recently, the technique of forced alignments has shown to improve translation quality for a phrase-based system, applying a more statistically sound approach to phrase extraction. In this work we investigate the first steps to combine forced alignment with a hierarchical model. Experimental results on IWSLT and WMT data show improvements in translation quality of up to 0.7% BLEU and 1.0% TER.