International Workshop on Spoken Language Translation (IWSLT) 2012
Transliteration is the process of writing a word (mainly
proper noun) from one language in the alphabet of another
language. This process requires mapping the pronunciation
of the word from the source language to the closest possible
pronunciation in the target language. In this paper we introduce
a new semi-supervised transliteration mining method
for parallel and comparable corpora. The method is mainly
based on a new suggested Three Levels of Similarity (TLS)
scores to extract the transliteration pairs. The first level calculates
the similarity of of all vowel letters and consonants
letters. The second level calculates the similarity of long
vowels and vowel letters at beginning and end position of
the words and consonants letters. The third level calculates
the similarity consonants letters only.
We applied our method on Arabic-English parallel and comparable corpora. We evaluated the extracted transliteration pairs using a statistical based transliteration system. This system is built using letters instead or words as tokens. The transliteration system achieves an accuracy of 0.50 and a mean F-score 0.8958 when trained on transliteration pairs extracted from a parallel corpus. The accuracy is 0.30 and the mean F-score 0.84 when we used instead a comparable corpus to automatically extract the transliteration pairs. This shows that the proposed semi-supervised transliteration mining algorithm is effective and can be applied to other language pairs. We also evaluated two segmentation techniques and reported the impact on the transliteration performance.
Full Paper Presentation
Bibliographic reference. Aransa, Walid / Schwenk, Holger / Barrault, Loic (2012): "Semi-supervised transliteration mining from parallel and comparable corpora", In IWSLT-2012, 185-192.