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Translational research (also called translation research, translational science, or, when the context is clear, simply translation) [1] [2] is research aimed at translating (converting) results in basic research into results that directly benefit humans. The term is used in science and technology, especially in biology and medical science.
A rendition of the Vauquois triangle, illustrating the various approaches to the design of machine translation systems.. The direct, transfer-based machine translation and interlingual machine translation methods of machine translation all belong to RBMT but differ in the depth of analysis of the source language and the extent to which they attempt to reach a language-independent ...
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.
Example-based machine translation (EBMT) is a method of machine translation often characterized by its use of a bilingual corpus with parallel texts as its main knowledge base at run-time. It is essentially a translation by analogy and can be viewed as an implementation of a case-based reasoning approach to machine learning .
These models differ from an encoder-decoder NMT system in a number of ways: [35]: 1 Generative language models are not trained on the translation task, let alone on a parallel dataset. Instead, they are trained on a language modeling objective, such as predicting the next word in a sequence drawn from a large dataset of text.
The statistical translation models were initially word based (Models 1-5 from IBM Hidden Markov model from Stephan Vogel [5] and Model 6 from Franz-Joseph Och [6]), but significant advances were made with the introduction of phrase based models. [7] Later work incorporated syntax or quasi-syntactic structures. [8]
Instead of training specialized translation models on parallel datasets, one can also directly prompt generative large language models like GPT to translate a text. [29] [30] [31] This approach is considered promising, [32] but is still more resource-intensive than specialized translation models.
For example, it might be trained just for Japanese-English and Korean-English translation, but can perform Japanese-Korean translation. The system appears to have learned to produce a language-independent intermediate representation of language (an " interlingua "), which allows it to perform zero-shot translation by converting from and to the ...