Search results
Results from the WOW.Com Content Network
By 2020, the system had been replaced by another deep learning system based on a Transformer encoder and an RNN decoder. [10] GNMT improved on the quality of translation by applying an example-based (EBMT) machine translation method in which the system learns from millions of examples of language translation. [2]
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]
The Translate Toolkit is a localization and translation toolkit. It provides a set of tools for working with localization file formats and files that might need localization. The toolkit also provides an API on which to develop other localization tools.
To use Google Translator Toolkit first, users uploaded a file from their desktop or entered a URL of a web page or Wikipedia article that they want to translate. Google Translator Toolkit automatically 'pretranslated' the document. It divided the document into segments, usually sentences, headers, or bullets.
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.
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 .
Bidirectional encoder representations from transformers (BERT) is a language model introduced in October 2018 by researchers at Google. [1] [2] It learns to represent text as a sequence of vectors using self-supervised learning.
The accuracy of Google Translate continues to improve, and in many cases approaches the accuracy of human translation; Use of non-English sources can help counter systemic bias on Wikipedia, which skews to Anglocentric and Eurocentric perspectives; Cons. Accuracy may not be sufficient for all uses, and human translation is still more accurate