WMT'16 English-German

material ID: wmt16englishgerman

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Publication: TIPSTER (2019)

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AbstractTransformer is the state-of-the-art model in recent machine translation evaluations. Two strands of research are promising to improve models of this kind: the first uses wide networks (a.k.a. Transformer-Big) and …...

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A Multilingual View of Unsupervised Machine Translation

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AbstractWe present a probabilistic framework for multilingual neural machine translation that encompasses supervised and unsupervised setups, focusing on unsupervised translation. In addition to studying the vanilla case where there is …...

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Shallow-to-Deep Training for Neural Machine Translation

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AbstractDeep encoders have been proven to be effective in improving neural machine translation (NMT) systems, but training an extremely deep encoder is time consuming. Moreover, why deep models help NMT …...

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Energy-Based Reranking: Improving Neural Machine Translation Using Energy-Based Models

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Publication: TIPSTER (2021)

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AbstractThe discrepancy between maximum likelihood estimation (MLE) and task measures such as BLEU score has been studied before for autoregressive neural machine translation (NMT) and resulted in alternative training algorithms …...

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