In this post Pat Nagle, our Project Manager at KantanMT speaks about Neural MT and the importance of using high quality data while training MT engines. He delves deep into the various ways in which KantanMT data can be used in order to get the best translation output. Continue reading
The Rosetta stone paradigm translations. Hans Hillewaert Wikimedia (CC)
This article is written by José Pichel Andrés and was originally published in Spanish in the online journal, El Español. It has been translated into English by Carlos Collantes from the Professional Services team at KantanMT. The article has been edited slightly for readability, but we have made all attempts possible to retain the original flavour of José’s article.
Researchers today are redefining Machine Translation. Though it is still a far cry from being completely satisfactory, it displays a rapid development, thanks to new systems like Neural Networks. Continue reading
Last month, Dr Dimitar Shterionov published an article on MultiLingual, a highly respected magazine on the localization industry, where he talks about the changing landscape of Machine Translation (MT) in the language industry, and how Neural Machine Translation (NMT) has very recently emerged as a revolutionary new paradigm in MT research.
Let us take a look at some of the key points put forward by Dimitar and what is happening with NMT. Thanks to increasingly robust technological advancements, Machine Translated content has proven to be incredibly accurate, especially when compared to the output of traditional MT models.
What is Artificial Neural Network (ANN) and Neural Machine Translation (NMT)? Continue reading