D08-1041 apply this approach to extract name transliteration spelling variants from bilingual
N10-1103 letter-to-phoneme conversion and ( 2 ) name transliteration . For the letter-to - phoneme
N09-1005 their performance in an end-to-end name transliteration task . We showed that consistent
N09-1005 their accuracies on a standard name transliteration task . 2 Background We follow
N10-1103 letter-to-phoneme conversion and name transliteration , have recently received much
D12-1002 showed its effectiveness on the name transliteration task . Our approach learns interlingual
D12-1002 and show its effectiveness for name transliteration task . The key idea of our approach
D08-1113 occurs in tasks as diverse as name transliteration , spelling correc - tion , pronunciation
D12-1002 as the bridge language . Since name transliteration problem is being studied for
D12-1002 paper , we relax the need for name transliterations by using international phonetic
N10-1078 system we used has no special name transliteration component , so often a name remains
N09-1005 and apply it to the U.S. Senator name transliteration task ( which we update to the
D12-1002 training data consisting of bilingual name transliterations ( orthographic name-to-name mappings
N09-3011 the real-world Arabic - English name transliteration task on a data set of 10,084
N10-1073 transformations from a training set of name transliterations in the two languages using the
N10-1103 letter-to-phoneme conversion and name transliteration , establishing a new state-of-the-art
N07-1046 state-of-the-art statistical Arabic name transliteration systems such as ( Al-Onaizan
P04-1021 de facto standard of personal name transliteration in today 's Chinese press . The
C04-1103 dictionary compilation , automatic name transliteration has become an indispensable component
N09-1048 in English-to-Chinese personal name transliteration . Matthews ( 2007 ) took transliteration
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