Joint generation of transliterations from multiple representations

Abstract

Machine transliteration is often referred to as phonetic translation. We show that transliterations incorporate information from both spelling and pronunciation, and propose an effective model for joint transliteration generation from both representations. We further generalize this model to include transliterations from other languages, and enhance it with reranking and lexicon features. We demonstrate significant improvements in transliteration accuracy on several datasets.