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Online relative margin maximization for statistical machine translation

By Vladimir Eidelman, Yuval Marton and Philip Resnik


Recent advances in large-margin learning have shown that better generalization can be achieved by incorporating higher order information into the optimization, such as the spread of the data. However, these so-lutions are impractical in complex struc-tured prediction problems such as statis-tical machine translation. We present an online gradient-based algorithm for rela-tive margin maximization, which bounds the spread of the projected data while max-imizing the margin. We evaluate our op-timizer on Chinese-English and Arabic-English translation tasks, each with small and large feature sets, and show that our learner is able to achieve significant im-provements of 1.2-2 BLEU and 1.7-4.3 TER on average over state-of-the-art opti-mizers with the large feature set.

Year: 2013
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