Evolution of modern machine translators: Difference between revisions
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On a basic level, MT performs simple substitution of words in one language for words in another, but that alone usually cannot produce a good translation of a text because recognition of whole phrases and their closest counterparts in the target language is needed. <ref>https://en.wikipedia.org/wiki/Machine_translation</ref> | On a basic level, MT performs simple substitution of words in one language for words in another, but that alone usually cannot produce a good translation of a text because recognition of whole phrases and their closest counterparts in the target language is needed. <ref>https://en.wikipedia.org/wiki/Machine_translation</ref> | ||
== | == Evolution of machine translators == | ||
* [https://medium.freecodecamp.org/a-history-of-machine-translation-from-the-cold-war-to-deep-learning-f1d335ce8b5 Please carefully study this resource. It is superb resource for the history of machine translation]. You should know the terms below at an '''outline''' level. Please do not use the abbreviations in the linked article in any IB answer. | * [https://medium.freecodecamp.org/a-history-of-machine-translation-from-the-cold-war-to-deep-learning-f1d335ce8b5 Please carefully study this resource. It is superb resource for the history of machine translation]. You should know the terms below at an '''outline''' level. Please do not use the abbreviations in the linked article in any IB answer. | ||
* Rule-based machine translation | |||
uses dictionaries and a set of linguistic rules to translate between two languages. <ref>Sheevankit the awesome</ref> | |||
* Example-based Machine Translation | |||
uses ready-made phrases as examples instead of repeated translation. <ref>Sheevankit the awesome</ref> | |||
* Statistical Machine Translation | |||
the machine tries to recognise patterns by studying similar texts without the need for dictionaries or rules. <ref>Sheevankit the awesome</ref> | |||
* Neural Machine Translation | |||
uses a large artificial neural network to predict the probability of a sequence of words.<ref>Sheevankit the awesome</ref> | |||
== Our ultimate goal == | == Our ultimate goal == | ||
Latest revision as of 10:53, 27 February 2019
HL content: Modeling & Simulation[1]
Machine translation, (sometimes referred to by the abbreviation MT) is a sub-field of computational linguistics that investigates the use of software to translate text or speech from one language to another.
On a basic level, MT performs simple substitution of words in one language for words in another, but that alone usually cannot produce a good translation of a text because recognition of whole phrases and their closest counterparts in the target language is needed. [2]
Evolution of machine translators[edit]
- Please carefully study this resource. It is superb resource for the history of machine translation. You should know the terms below at an outline level. Please do not use the abbreviations in the linked article in any IB answer.
- Rule-based machine translation
uses dictionaries and a set of linguistic rules to translate between two languages. [3]
- Example-based Machine Translation
uses ready-made phrases as examples instead of repeated translation. [4]
- Statistical Machine Translation
the machine tries to recognise patterns by studying similar texts without the need for dictionaries or rules. [5]
- Neural Machine Translation
uses a large artificial neural network to predict the probability of a sequence of words.[6]
Our ultimate goal[edit]
Instant, perfectly accurate translation. Click here for a funny take on this
Standards[edit]
- Outline the evolution of modern machine translators.
References[edit]
- ↑ http://www.flaticon.com/
- ↑ https://en.wikipedia.org/wiki/Machine_translation
- ↑ Sheevankit the awesome
- ↑ Sheevankit the awesome
- ↑ Sheevankit the awesome
- ↑ Sheevankit the awesome