Microsoft adds 13 new African languages to its translator service
Microsoft has added 13 new African languages to its Microsoft Azure Cognitive Services Translator, allowing text and documents to be translated to and from these languages across the entire Microsoft ecosystem of products and services.
The new languages include chiShona, Hausa, Igbo, Kinyarwanda, Lingala, Luganda, Nyanja, Rundi, Sesotho, Sesotho sa Leboa, Setswana, Xhosa, and Yoruba.
This brings the total number of supported languages to 124, after adding Somali and Zulu last year. Microsoft is taking a step toward ensuring expanded language support for millions of people in Africa and around the world.
“It is transformative when we can empower our communities across the continent to do and achieve more, and even more so when they can do it in their own language. Through this release, we continue to build meaningful cognitive products and services that improve accessibility and break down the language barrier between people and cultures all over the world,” says Wael Elkabbany, General Manager, and Microsoft Africa Regional Cluster.
Using Translator, people and organizations can add African languages’ text translation to apps, websites, workflows, and tools; or use Translator’s Document Translation feature to translate entire documents, or volumes of documents, in a variety of different file formats preserving their original formatting.
They can also use Translator with Cognitive Services such as Speech or Computer Vision to add additional capabilities such as speech-to-text and image translation into their apps. Educators can create a more inclusive classroom for both students and parents with live captioning and cross-language understanding.
Microsoft has continuously added languages and dialects to its Translator service while ensuring the translation quality of the supported languages by using the latest neural machine translation (NMT) techniques.
“We achieve this by working with partners in language communities who can help gather data for specific languages and who have access to human-translated texts also helps to overcome the challenge of obtaining enough bilingual data to train and produce a machine translation model. This network of partners helps collect bilingual data, consult with community members, and evaluate the quality of the resulting machine translation models,” adds Elkabbany.
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