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Aug 7, 2019 · Neural machine translation, or NMT for short, is the use of neural network models to learn a statistical model for machine translation. The key benefit to the approach is that a single system can be trained directly on source and target text, no longer requiring the pipeline of specialized systems used in statistical machine learning.
Jan 1, 2020 · Neural machine translation has become the dominant approach to machine translation in both research and practice. This article reviewed the widely used methods in NMT, including modeling, decoding, data augmentation, interpretation, as well as evaluation.
Neural machine translation. Neural machine translation (NMT) is an approach to machine translation that uses a large artificial neural network to predict the likelihood of a sequence of words, typically modeling entire sentences in a single integrated model.
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Jul 9, 2021 · Abstract. Deep neural networks (DNN) have achieved great success in several research areas like information retrieval, image processing, and speech recognition. In the field of machine translation, neural machine translation (NMT) has been able to overcome the statistical machine translation (SMT), which has been the dominant technology for a ...
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 both in training and in translation inference.
Nov 22, 2016 · In September, we announced that Google Translate is switching to a new system called Google Neural Machine Translation (GNMT), an end-to-end learning framework that learns from millions of examples, and provided significant improvements in translation quality.
Abstract. The field of machine translation (MT), the automatic translation of written text from one natural language into another, has experienced a major paradigm shift in recent years. Statistical MT, which mainly relies on various count-based models and which used to dominate MT research for decades, has largely been superseded by neural ...
