Hope and Fear for Discriminative Training of Statistical Translation Models
David Chiang; 13(40):1159−1187, 2012.
In machine translation, discriminative models have almost entirely supplanted the classical noisy-channel model, but are standardly trained using a method that is reliable only in low-dimensional spaces. Two strands of research have tried to adapt more scalable discriminative training methods to machine translation: the first uses log-linear probability models and either maximum likelihood or minimum risk, and the other uses linear models and large-margin methods. Here, we provide an overview of the latter. We compare several learning algorithms and describe in detail some novel extensions suited to properties of the translation task: no single correct output, a large space of structured outputs, and slow inference. We present experimental results on a large-scale Arabic-English translation task, demonstrating large gains in translation accuracy.
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