Everybody loves a rich cousin: An empirical study of transliteration through bridge languages

Most state of the art approaches for machine transliteration are data driven and require sig-nificant parallel names corpora between lan-guages. As a result, developing translitera-tion functionality among n languages could be a resource intensive task requiring paral-lel names corpora in the order of nC2. In this paper, we explore ways of reducing this high resource requirement by leveraging the avail-able parallel data between subsets of the n lan-guages, transitively. We propose, and show empirically, that reasonable quality transliter-ation engines may be developed between two languages, X and Y, even when no direct par-allel names data exists between them, but only transitively through language Z. Such sys-tems alleviate the need for O(nC2) corpora, significantly. In addition we show that the per-formance of such transitive transliteration sys-tems is in par with direct transliteration sys-tems, in practical applications, such as CLIR systems.