We present a discriminative substring decoder for transliteration. This decoder extends recent approaches for discriminative character transduction by allowing for a list of known target-language words, an important resource for transliteration. Our approach improves upon Sherifand Kondrak\u2019s (2007b) state-of-the-art decoder, creating a 28.5% relative improvement 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 hybrid outperforms its purely discriminative counterpart, despite losing access to rich source-context features. Finally, we show that machine transliterations have a positive impact on machine translation quality, improving human judgments by 0.5 on a 4-point scale.<\/p>\n","protected":false},"excerpt":{"rendered":"
We present a discriminative substring decoder for transliteration. This decoder extends recent approaches for discriminative character transduction by allowing for a list of known target-language words, an important resource for transliteration. Our approach improves upon Sherifand Kondrak\u2019s (2007b) state-of-the-art decoder, creating a 28.5% relative improvement in transliteration accuracy on a Japanese katakana-to-English task. 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