Akiva Miura

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Active learning is a framework that makes it possible to efficiently train statistical models by selecting informative examples from a pool of unlabeled data. Previous work has found this framework effective for machine translation (MT), making it possible to train better translation models with less effort, particularly when annotators translate short(More)
Pivot translation allows for translation of language pairs with little or no parallel data by introducing a third language for which data exists. In particular, the trian-gulation method, which translates by combining source-pivot and pivot-target translation models into a source-target model, is known for its high translation accuracy. However, in the(More)
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