Transliterating words and names from one language to another is a frequent and highly productive phenomenon. For example, English word cache is transliterated in Japanese asキャッシュ “kyasshu”. In many cases, recent transliterations are not recorded in machine readable dictionaries so it is impossible to rely on dictionary lookup to find transliteration equivalents. In this paper we describe a meth...

Machine Transliteration is an essential task for many NLP applications. However, names and loan words typically originate from various languages, obey different transliteration rules, and therefore may benefit from being modeled independently. Recently, transliteration models based on Bayesian learning have overcome issues with over-fitting allowing for many-to-many alignment in the training of...

Effective transliteration of proper names via grapheme conversion needs to find transliteration patterns in training data, and then generate optimized candidates for testing samples accordingly. However, the top-1 accuracy for the generated candidates cannot be good if the right one is not ranked at the top. To tackle this issue, we propose to rerank the output candidates for a better order usi...

We propose a novel HMM-based framework to accurately transliterate unseen named entities. The framework leverages features in letteralignment and letter n-gram pairs learned from available bilingual dictionaries. Letter-classes, such as vowels/non-vowels, are integrated to further improve transliteration accuracy. The proposed transliteration system is applied to out-of-vocabulary named-entitie...

Automatic transliteration problem is to transcribe foreign words in one’s own alphabet. Machine generated transliteration can be useful in various applications such as indexing in an information retrieval system and pronunciation synthesis in a text-to-speech system. In this paper we present a model for statistical Englishto-Korean transliteration that generates transliteration candidates with ...

This paper classifies the problem of machine transliteration into four types, i.e., forward/backward transliteration between same/different character sets, based on transliteration direction and character sets. A phoneme-based similarity measure is proposed to deal with backward transliteration between different character sets. Chinese-English information retrieval is taken as an example. The e...

This paper reports on our participation in the NEWS 2011 shared task on transliteration generation with a syllable-based Backward Maximum Matching system. The system uses the Onset First Principle to syllabify English names and align them with Chinese names. The bilingual lexicon containing aligned segments of various syllable lengths subsequently allows direct transliteration by chunks. The of...

The Character Sequence Modeling (CSM), typically called the Language Modeling, has not received sufficient attention in the current transliteration research. We discuss the impact of various CSM factors like word granularity, smoothing technique, corpus variation, and word origin on the transliteration accuracy. We demonstrate the importance of CSM by showing that for transliterating into Engli...