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Researcher profile

Wolfgang Macherey

7 papers in the PaperMetrix corpus

Publications

Papers by this author

  1. Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

    2016 · arXiv (Cornell University)

    Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation, with the potential to overcome many of the weaknesses of conventional phrase-based translation systems. Unfortunately, NMT systems are known to be computationally expensive …

  2. Zero-Shot Cross-lingual Classification Using Multilingual Neural Machine Translation

    2018 · arXiv (Cornell University)

    Transferring representations from large supervised tasks to downstream tasks has shown promising results in AI fields such as Computer Vision and Natural Language Processing (NLP). In parallel, the recent progress in Machine Translation (MT) has …

  3. The Missing Ingredient in Zero-Shot Neural Machine Translation

    2019 · arXiv (Cornell University)

    Multilingual Neural Machine Translation (NMT) models are capable of translating between multiple source and target languages. Despite various approaches to train such models, they have difficulty with zero-shot translation: translating between language pairs that were …

  4. Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling

    2019 · arXiv (Cornell University)

    Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models. Lingvo models are composed of modular building blocks that are flexible and easily extensible, …

  5. Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges

    2019 · arXiv (Cornell University)

    We introduce our efforts towards building a universal neural machine translation (NMT) system capable of translating between any language pair. We set a milestone towards this goal by building a single massively multilingual NMT model …

  6. Leveraging Weakly Supervised Data to Improve End-to-end Speech-to-text Translation

    2019

    End-to-end Speech Translation (ST) models have many potential advantages when compared to the cascade of Automatic Speech Recognition (ASR) and text Machine Translation (MT) models, including lowered inference latency and the avoidance of error compounding. …

  7. Direct Speech-to-Speech Translation with a Sequence-to-Sequence Model

    2019

    We present an attention-based sequence-to-sequence neural network which can directly translate speech from one language into speech in another language, without relying on an intermediate text representation.The network is trained end-to-end, learning to map speech …