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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.
Jun 1, 2021 · PDF | Neural machine translation (NMT) is an approach to machine translation (MT) that uses deep learning techniques, a broad area of machine learning... | Find, read and cite all the...
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- 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which mainly relies on various count-based models and which used to dominate MT research for decades, has largely been superseded by neural ma-chine translat...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 ...
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 ...
- Wandri Jooste, Rejwanul Haque, Andy Way
- 2021
Google Translate is a multilingual neural machine translation service developed by Google to translate text, documents and websites from one language into another. It offers a website interface, a mobile app for Android and iOS, as well as an API that helps developers build browser extensions and software applications. [3] .
Multilingual Neural Machine Translation (MNMT) models are commonly trained on a joint set of bilingual corpora which is acutely English-centric (i.e. English either as the source or target language). While direct data between two languages that are non-English is explicitly available at times, its use is not common.
Machine translation (MT) is an important sub-field of natural language processing that aims to translate natural languages using computers. 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.
