NCU IISR English-Korean and English-Chinese Named Entity Transliteration Using Different Grapheme Segmentation Approaches

Abstract

This paper describes our approach to English-Korean and English-Chinese transliteration task of NEWS 2015. We use different grapheme segmentation approaches on source and target languages to train several transliteration models based on the M2M-aligner and DirecTL+, a string transduction model. Then, we use two reranking techniques based on string similarity and web co-occurrence to select the best transliteration among the prediction results from the different models. Our English-Korean standard and non-standard runs achieve 0.4482 and 0.5067 in top-1 accuracy respectively, and our English-Chinese standard runs achieves 0.2925 in top-1 accuracy. c 2015 Association for Computational Linguistics.