Discriminative substring decoding for transliteration

We present a discriminative substring de-coder for transliteration. This decoder extends recent approaches for discrimi-native character transduction by allow-ing for a list of known target-language words, an important resource for translit-eration. Our approach improves upon Sherif and Kondrak’s (2007b) state-of-the-art decoder, creating a 28.5 % relative im-provement in transliteration accuracy on a Japanese katakana-to-English task. We also conduct a controlled comparison of two feature paradigms for discriminative training: indicators and hybrid generative features. Surprisingly, the generative hy-brid outperforms its purely discriminative counterpart, despite losing access to rich source-context features. Finally, we show that machine transliterations have a posi-tive impact on machine translation quality, improving human judgments by 0.5 on a 4-point scale.