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T5 (Text-to-Text Transfer Transformer) is a series of large language models developed by Google AI introduced in 2019. [ 1 ] [ 2 ] Like the original Transformer model, [ 3 ] T5 models are encoder-decoder Transformers , where the encoder processes the input text, and the decoder generates the output text.
Moses is a statistical machine translation engine that can be used to train statistical models of text translation from a source language to a target language, developed by the University of Edinburgh. [2] Moses then allows new source-language text to be decoded using these models to produce automatic translations in the target
Forcada and Ñeco simplified this procedure in 1997 to directly train a source encoder and a target decoder in what they called a recursive hetero-associative memory. [11] Also in 1997, Castaño and Casacuberta employed an Elman's recurrent neural network in another machine translation task with very limited vocabulary and complexity. [12] [13]
Unbabel, a tech company that provides both machine and human-based translation services for businesses, has created a new AI model that it says beats OpenAI’s GPT-4o and other commercially ...
A freelance translator says AI makes him more efficient at his job and it won't replace him.
The following table compares the number of languages which the following machine translation programs can translate between. (Moses and Moses for Mere Mortals allow you to train translation models for any language pair, though collections of translated texts (parallel corpus) need to be provided by the user.
Google Translate's NMT system uses a large artificial neural network capable of deep learning. [1] [2] [3] By using millions of examples, GNMT improves the quality of translation, [2] using broader context to deduce the most relevant translation.
GPT-2's training corpus included virtually no French text; non-English text was deliberately removed while cleaning the dataset prior to training, and as a consequence, only 10MB of French of the remaining 40,000MB was available for the model to learn from (mostly from foreign-language quotations in English posts and articles). [2]
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