SharePoint itself supports 51 languages. PointFire 365 supports all of those languages. All user interface elements can be shown in any of those languages, all content can be filtered to show content to users in any of those languages. They are indicated by a "Y" in the "SharePoint" column.
However the quality of machine translation can vary. In the back end, PointFire Power Translator uses one of four different translation technologies, powered by Azure translation technologies. The technology that most people are used to, which powered the old Bing and Google translation engines, is statistical machine translation. This was the state of the art until a few years ago, using syntax-based statistical translation models with a few additional tricks to improve language quality. It trains on large corpora of text that is already translated, trying to mimic the translation process using statistics. This technology got better and better over the years. For several languages we are still using these statistical models.
Around 2015, based in large part on algorithms developed at the University of Toronto and Université de Montréal, Neural Network models emerged as a better alternative to statistical models. These deep networks require enormous amounts of computing power to train. Interestingly enough, large software companies like Microsoft and Google tend to publish their results and make their insights and tools available to each other, so that they can each improve on one another's work. Because of this, neural machine translation technology has progressed quickly. It still requires massive amounts of computing power to train such models, something like 100 processors for a week for each training run, but Microsoft is a leader in technologies to use less processing power so it uses a fraction of that. For most major languages, PointFire is using neural translation.
Then in March 2018 Microsoft announced it had achieved "Human Parity" for some translation tasks. Mind you this is a controversial claim and neither the humans with whom parity was achieved nor the people doing the rating of translation quality were professional translators, but we use the "HP" label to refer to the technology being used, not necessarily to the quality level. These initial engines were not suitable for production, they were far too massive. Microsoft then improved on the size and performance of these complex models, using groundbreaking techniques. For example they use a large deep neural network to train a much faster wide shallow network, gaining a huge performance improvement and
improved translation quality. They train a separate neural network to detect and correct errors in the input data. They use the trick that so many of us have used to hilarious effect: it translates sentences from English to another language, then translates that translation back to English to see whether it is the same. These new "human parity" engines have been sweeping international competitions of translation quality and, as opposed to a lot of the other entries, these are available in production. Chinese and German were released in late November 2018, and French, Hindi, Italian, Spanish, Japanese, Korean and Russian, to/from English are now available. More details on the Microsoft Translator blog
if you are interested.
The highest quality of any translation engine on earth is still not good enough for you? We offer an even higher quality. Using Microsoft's Custom Translator, you can re-train an existing neural machine translation engine using your own professionally translated documents so that it adopts your vocabulary and your style. If you're keen, you can even train it on a different dialect or language. PointFire Power Translator supports these custom models as well as the standard ones in the table above.
There is a lot of overlap between the languages supported by PointFire 365 and those supported by PointFire Power Translator. Some of them are the same language, but some of them have a mapping that you may need to be aware of. For example, SharePoint supports Dari but not Persian, while the translator supports Persian and not Dari. Written Persian and written Dari are close enough that we have declared them to be the same. When you check the translations, keep it in mind. SharePoint supports two versions of Portuguese. The translator supports only one. It is a hybrid but it looks more like Brazilian Portuguese. We use the same engine for both, but it's a good idea to check the translations. For Norwegian, SharePoint explicitly uses bokmål, while the translator uses a different language code that may refer to nynorsk. From the limited knowledge of Norwegian available to our team, the language code looks like a mistake and we believe that both use bokmål.
The Balkans present their own challenges. Certain languages of former Yugoslavia are similar to certain other languages. It's a sensitive subject so we will not discuss in writing how we bridge some of the gaps between language codes. However it has been made clear to us that using a Bulgarian translation engine for the Macedonian language is not acceptable and therefore we do not provide machine translation functionality for Macedonian, until a compatible translation engine specific to the Republic of North Macedonia is available.
There are a number of languages for which PointFire Power Translator can provide translations of SharePoint or OneDrive documents, but which SharePoint itself does not support. The Item Language column of these translations will be tagged with the language code of these languages, whether or not it is an allowable language code in PointFire 365. Be aware that PointFire 365 will not filter these documents. These languages include Icelandic, Kiswahili, and Maltese. There are in theory two versions of Klingon, one that uses the Latin alphabet and the other that uses the Klingon scripts. We regret that the version using the Klingon font, if you have installed this font, is no longer working :-( and we have not invested the time to find out why.