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  1. Google神經機器翻譯系統 - 维基百科,自由的百科全书 › wiki › Google神經機器翻譯系統

    Google神經機器翻譯系統(英語: Google Neural Machine Translation ,简写:GNMT),是Google開發的神經機器翻譯(NMT)系統,於2016年11月推出,它使用人工神經網絡來提高Google翻譯的流暢度和準確性。

  2. Self-Supervised Neural Machine Translation › anthology › P19-1178

    stest2014 using English and French Wikipedia data for training. 1 Introduction Neural machine translation (NMT) has brought major improvements in translation quality (Cho et al.,2014;Bahdanau et al.,2014;Vaswani et al., 2017). Until recently, these relied on the avail-ability of high-quality parallel corpora. As such

  3. Wikipedia Sentence Simplification - Stanford University › class › archive

    borrowed from neural machine translation systems with transformations at the sentence level. Trans-formers and multi-head self-attention have revolutionized NLP progress. In this project, we utilize transformer architecture in the sentence simplification task, integrating into it a paraphrase database

  4. Document Sub-structure in Neural Machine Translation | DeepAI › publication › document-sub-structure-in

    Dec 13, 2019 · Considerable progress has been made in machine translation (MT) thanks to the use of neural MT (NMT). While most NMT systems translate on the level of individual sentences (following similar practices in statistical MT), there has been significant interest in recent years in using the document context to improve translation [Hardmeier2014, Bawden2018, Wang2019].

  5. Neural Machine Translation for Extremely Low-Resource African Languages: A Case Study on Bambara Allahsera Auguste Tapo 1;, Bakary Coulibaly 2, Sébastien Diarra , Christopher Homan , Julia Kreutzer3, Sarah Luger4, Arthur Nagashima 1, Marcos Zampieri , Michael Leventhal2 1Rochester Institute of Technology

  6. Apr 01, 2020 · Neural Machine Translation English to French Translation - seq2seq. This neural machine translation tutorial trains a seq2seq model on a set of many thousands of English to French translation pairs to translate from English to French. It provides an intrinsic/extrinsic comparison of various sequence-to-sequence (seq2seq) models in translation.

  7. Oct 11, 2019 · In “Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges” and follow-up papers [4,5,6,7], we push the limits of research on multilingual NMT by training a single NMT model on 25+ billion sentence pairs, from 100+ languages to and from English, with 50+ billion parameters. The result is an approach for ...

  8. Quality Estimation Task - Statistical Machine Translation › wmt20 › quality-estimation-task

    This shared task will build on its previous editions to further examine automatic methods for estimating the quality of neural machine translation output at run-time, without relying on reference translations. As in previous years, we cover estimation at various levels.

  9. Exploring Language Models for Neural Machine Translation ... › exploring-language-models-for

    Jun 24, 2020 · The main aim of this article is to introduce you to language models, starting with neural machine translation (NMT) and working towards generative language models. For the purposes of this tutorial, even with limited prior knowledge of NLP or recurrent neural networks (RNNs), you should be able to follow along and catch up with these state-of ...

  10. Machine Learning Papers Summary - GitHub Pages › machine-learning-papers-summary

    Reading Wikipedia to Answer Open-Domain Questions (2017), Danqi Chen et al. [ summary] Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling (2016), Bing Liu et al. [ summary] Neural Machine Translation by Jointly Learning to Align and Translate (2016), D. Bahdanau et al. [ summary]

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