Discriminative Substring Decoding for Transliteration

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 Sherif and Kondrak's (2007b) state-of-theart 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.

• Type: Journal
• Year: 2009
• Where: EMNLP
• Authors: Colin Cherry, Hisami Suzuki
• Keywords: Discriminative Character Transduction, Discriminative Counterpart, Discriminative Substring Decoder, EMNLP 2009, Natural Language Processing