Maja Popović

Learn More
Evaluation and error analysis of machine translation output are important but difficult tasks. In this work, we propose a novel method for obtaining more details about actual translation errors in the generated output by introducing the decomposition of Word Error Rate (WER) and Position independent word Error Rate (PER) over different Part-of-Speech (POS)(More)
We present a pilot study on an evaluation method which is able to rank translation outputs with no reference translation, given only their source sentence. The system employs a statistical classifier trained upon existing human rankings, using several features derived from analysis of both the source and the target sentences. Development experiments on one(More)
Current metrics for evaluating machine translation quality have the huge drawback that they require human-quality reference translations. We propose a truly automatic evaluation metric based on IBM1 lexicon probabilities which does not need any reference translations. Several variants of IBM1 scores are systematically explored in order to find the most(More)
Evaluation of machine translation output is an important but difficult task. Over the last years, a variety of automatic evaluation measures have been studied, some of them like Word Error Rate (WER), Position Independent Word Error Rate (PER) and BLEU and NIST scores have become widely used tools for comparing different systems as well as for evaluating(More)
In this work, we examine the quality of several statistical machine translation systems constructed on a small amount of parallel Serbian-English text. The main bilingual parallel corpus consists of about 3k sentences and 20k running words from an unrestricted domain. The translation systems are built on the full corpus as well as on a reduced corpus(More)
RWTH participated in the shared translation task of the Fourth Workshop of Statistical Machine Translation (WMT 2009) with the German-English, French-English and Spanish-English pair in each translation direction. The submissions were generated using a phrase-based and a hierarchical statistical machine translation systems with appropriate morpho-syntactic(More)
  • 1