Integrating an Unsupervised Transliteration Model into Statistical Machine Translation
Nadir Durrani, Hassan Sajjad, Hieu Hoang, Philipp Koehn · 2014
We investigate three methods for integrating an unsupervised transliteration model into an end-to-end SMT system.We induce a transliteration model from parallel data and use it to translate OOV words.Our approach is fully unsupervised and language independent.In the methods to integrate transliterations, we observed improvements from 0.23-0.75(∆ 0.41) BLEU points across 7 language pairs.We also show that our mined transliteration corpora provide better rule coverage and translation quality compared to the gold standard transliteration corpora.