‍ Using multiple machine translation APIs is the keyĪll the companies that have machine translation feature in their product or deal with voice technology for their customers have to use multiple machine translation APIs. If you have customers coming from different fields, you must consider this detail and optimize your choice. This means that MT APIs will perform better for text in medical field, other in automotive field, other in generic fields, etc. Some APIs are trained their engine with specific data. This is explained because of the quality of the text (we can imagine that tweets are lower quality texts compared to scientific papers or press articles for example). For example, some MT APIs perform better with text coming from tweets, others perform better with text from scientific papers, others with text from customer reviews, etc. When testing multiple machine translation APIs, you will find that providers accuracy can be different according to text quality and format. Performance variations according to text data quality You can find APIs that allow you to process text in Gujarati, Marathi, Burmese, Pashto, Zulu, Swahili, etc.

Rare language speciality: some machine translation vendors care about rare languages and dialects.For example: some MT APIs perform well for translating english (US, UK, Canada, South Africa, Singapore, Hong Kong, Ghana, Ireland, Australia, India, etc.), other for translating spanish (Spain, Argentina, Bolivia, Chile, Cuba, Equatorial Guinea, Laos, Peru, US, etc.), other are specialized in asian languages, etc. Region specialities: some MT APIs improve their machine translation APIs to make them accurate for text in specific language.In fact, some providers are specialized in specific languages. Machine Translation APIs perfom differently depending the language of audio. Performance variations according to the languages The MT market is dense and all those providers have their benefits and weaknesses. ‍ Performance variations of Machine Translation APIsįor all the companies who use machine translation in their softwares and for their customers, cost and performances are real concerns. There are many actors and here are actors that perform well (in alphabetical order): MT experts at Eden AI tested, compared and used many machine translation APIs of the market. A translation of the text can be obtained quickly and in a form that enables the recipients to understand the core content of the message email and communication: help decrease or even eliminate the language barrier in communication.documentation and instructions: translate PowerPoint presentations, intranet bulletins, and other similar documents.You can use Machine Translation in numerous fields, here are some examples of common use cases: Current machine translation software often allows for customization by domain or profession, improving output by limiting the scope of allowable substitutions. There are three types of machine translation methods: rule-based, statistical and neural networks. Machine Translation (MT) or automated translation is a process when a computer software translates text from one language to another without human involvement.

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