Sequence to sequence transduction is a general problem, for which many other problems are special cases. I also highlight some challenges of this general problem.
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We present useful tools for machine translation research: MTData, NLCodec, and RTG. We demonstrate their usefulness by creating a multilingual neural machine translation model capable of translating from 500 source languages to English. We make this multilingual model readily downloadable and usable as a service, or as a parent model for transfer-learning to even lower-resource languages.
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We explore the simple type-based classifier metric, \maf1, and study its applicability to MT evaluation. \
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We cast neural machine translation (NMT) as a classification task in an autoregressive setting and analyze the limitations of both classification and autoregression components. \
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