DFKI System Combination with Sentence Ranking at ML4HMT-2011

We present a pilot study on a Hybrid Machine Translation system that takes advantag e of multilateral system-specific metadata provided as part of the shared task. The proposed solution offers a machine learning approach, resulting into a selection mechanism able to learn and rank system outputs on the sentence level, based on their quality. For training, due to the lack of human annotations, word-level Levenshtein distance has been used as a quality indicator, whereas a rich set of sentence features was extracted and selected from the dataset. Three classification algo

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