The report provides a broad and in-depth look at the landscape of current machine translation vendors and analyzes how they perform against each other, and gives businesses insight on how to best leverage this information in their own strategies.
The study provides a wealth of information, for which this article hopes to provide a useful summary. Without further ado, let’s dive in.
There is a robust supply of machine translation engines on the market today; this report evaluated 31 of them. This includes the much-talked about large language model from Meta, No Language Left Behind, which was released as an open-source resource earlier this year, and boasts support for 200 languages.
You can learn more about the NLLB here: No Language oman mobile database Left Behind: Meta’s massive multilingual machine translation ambition pays it forward
This year sees a huge expansion in terms of the number of language pairs covered in total. Last year’s total was at 26,000 unique language pairs; this year, an additional 125,075 have been added.
Choosing evaluation parameters
This year, they evaluated MT engines across 11 language pairs and 9 different domains. For the purposes of evaluation, the researchers used only the stock models of each MT engine. This means that they are trained on generic data and are not focused on any particular domain.