We present a method for performing machine transliteration without any parallel resources. We frame the transliteration task as a decipherment problem and show that it is possible to learn cross-language phoneme mapping tables using only monolingual resources. We compare various methods and evaluate their accuracies on a standard name transliteration task. 1

We present a Hebrew to English transliteration method in the context of a machine translation system. Our method uses machine learning to determine which terms are to be transliterated rather than translated. The training corpus for this purpose includes only positive examples, acquired semi-automatically. Our classifier reduces more than 38 % of the errors made by a baseline method. The identified terms are then transliterated. We present an SMTbased transliteration model trained with a parallel corpus extracted from Wikipedia using a fairly simple method which requires minimal knowledge. The correct result is produced in more than 76 % of the cases, and in 92 % of the instances it is one of the top-5 results. We also demonstrate a small improvement in the performance of a Hebrew-to-English MT system that uses our transliteration module. 1

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vii Preface

workshop. The shared task features machine transliteration of proper names from English to 11 languages and from 3 languages to English. In total, 14 tasks are provided. 10 teams from 7 different countries participated in the evaluations. Finally, 73 standard and 4 non-standard runs are submitted, where diverse transliteration methodologies are explored and reported on the evaluation data. We report the results with 4 performance metrics. We believe that the shared task has successfully achieved its objective by providing a common benchmarking platform for the research community to evaluate the state-of-the-art technologies that benefit the future research and development. 1

(NEWS 2012), an ACL 2012 workshop. The shared task features machine transliteration of proper names from English to 11 languages and from 3 languages to English. In total, 14 tasks are provided. 7 teams participated in the evaluations. Finally, 57 standard and 1 non-standard runs are submitted, where diverse transliteration methodologies are explored and reported on the evaluation data. We report the results with 4 performance metrics. We believe that the shared task has successfully achieved its objective by providing a common benchmarking platform for the research community to evaluate the state-of-the-art technologies that benefit the future research and development. 1