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Neural machine translation. Neural machine translation ( NMT) is an approach to machine translation that uses an artificial neural network to predict the likelihood of a sequence of words, typically modeling entire sentences in a single integrated model. It is the dominant approach today [1] : 293 [2] : 1 and can produce translations that rival ...
- Abstract
- 1. Nomenclature
- = (x1; : : : ; xI) I 2 src
- 5. Encoder-Decoder Networks with Fixed Length Sentence Encodings
- 11.1. Cross-entropy Training
- ; x) = LCE(x; y; ): (37) j=1
- 11.6. Dual Supervised Learning
- 11.7. Adversarial Training
- 19. Conclusion
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which mainly relies on various count-based models and which used to dominate MT research for decades, has largely been superseded by neural ma-chine translat...
We will denote the source sentence of length I as x. We use the subscript i to index tokens in the source sentence. We refer to the source language vocabulary as src.
The translation of source sentence x into the target language is denoted as y. We use an analogous nomenclature on the target side.
Kalchbrenner and Blunsom [9] were the first who conditioned the target sentence distribution on a distributed fixed-length representation of the source sentence. Their recurrent continuous translation models (RCTM) I and II gave rise to a new family of so-called encoder-decoder networks which is the current prevailing architecture for NMT. Encoder-...
The most common objective function for NMT training is cross-entropy loss. The optimization problem over model parameters this loss is defined as follows: for a single sentence pair (x; y) under jyj arg min LCE(x; y; ) = arg min
Cross-entropy loss optimizes a Monte Carlo approximation of the cross-entropy to the real sequence-level distribution. Another intuition behind the cross-entropy loss is that we want to find model parameters that make the model distribution P ( jx) similar to the real distribution P( jx) over translations for a source sentence x. The similarity is ...
Recall that NMT networks are trained to model the distribution P(yjx) over transla-tions y for a given source sentence x. This training objective takes only one translation direction into account – from the source language to the target language. However, the chain rule gives us the following relation:
Generative adversarial networks [338, GANs] have recently become extremely popular in computer vision. GANs were originally proposed as framework for training generative models. For example, in computer vision, a generative model G would generate images that are similar to the ones in the training corpus. The input to a classic GAN is noise which i...
Neural machine translation (NMT) has become the de facto standard for large-scale machine translation in a very short period of time. This article traced back the origin of NMT to word and sentence embeddings and neural language models. We reviewed the most commonly used building blocks of NMT architectures – recurrence, convolution, and attention ...
People also ask
What is neural machine translation (NMT)?
Is there a neural network for machine translation?
What is end-to-end neural machine translation (NMT)?
Does Google Translate use neural machine translation?
In recent years, end-to-end neural machine translation (NMT) has achieved great success and has become the new mainstream method in practical MT systems. In this article, we first provide a broad review of the methods for NMT and focus on methods relating to architectures, decoding, and data augmentation.
- Zhixing Tan, Shuo Wang, Zonghan Yang, Gang Chen, Xuancheng Huang, Maosong Sun, Yang Liu
- 2020
DeepL Translator is a neural machine translation service that was launched in August 2017 and is owned by Cologne -based DeepL SE. The translating system was first developed within Linguee and launched as entity DeepL. It initially offered translations between seven European languages and has since gradually expanded to support 32 languages.
- 31 languages
- DeepL SE
- 28 August 2017; 6 years ago
Jun 30, 2020 · Neural machine translation (NMT) is an approach to machine translation (MT) that uses deep learning techniques, a broad area of machine learning based on deep artificial neural networks (NNs). The book Neural Machine Translation by Philipp Koehn targets a broad range of readers including researchers, scientists, academics, advanced undergraduate or postgraduate students, and users of MT ...
- Wandri Jooste, Rejwanul Haque, Andy Way
- 2021
Google Translate is a web-based free-to-user translation service developed by Google in April 2006. [11] It translates multiple forms of texts and media such as words, phrases and webpages. Originally, Google Translate was released as a statistical machine translation service. [11] The input text had to be translated into English first before ...
ArXiv. 2016. TLDR. GNMT, Google's Neural Machine Translation system, is presented, which attempts to address many of the weaknesses of conventional phrase-based translation systems and provides a good balance between the flexibility of "character"-delimited models and the efficiency of "word"-delicited models. Expand.
