Multilingual Transliteration for Pan-Indic Keyboard Input

Abstract

Millions of multilingual multi-Indic+English users would benefit if mobile text input eschewed manual switching between monolingual transliteration keyboards. We propose to make neural transliteration tagless to enable multilingual inference, and leverage multi-word training to build up a stronger internal language model (LM) within the associated sequence-to-sequence architecture. Coupling that approach with appropriate filtering to promote or demote multilingual candidates based on external constraints paves the way toward a single pan-Indic keyboard. Experiments comparing monolingual and multilingual typing experience show that such alternative is viable to replace separate language-specific keyboards.