Omniscien Blog
The Omniscien Advantage – We wrote the leading academic books on machine translation!!
Translation and language processing technologies have evolved substantially over the last decade. The Omniscien team has been at the forefront of research and development, leading the way with a comprehensive set of integrated tools, features, and technologies that are powered by and drive artificial intelligence and machine learning.
Behind many of the tools design is Omniscien’s Chief Scientist, Professor Philipp Koehn who leads our team of researchers and developers. Philipp is a pioneer in the machine translation space, his books on Statistical Machine Translation and Neural Machine Translation are the leading academic textbooks globally on machine translation. Both books are available now from Amazon.com or leading book stores.
How many languages are there in the world?
How many languages are there in the world? We have collected a list of languages with nearly 9,000 entries.
What is new in Language Studio 6.0
Language Studio 6.0 is a full rewrite with the knowledge gained over 14 years. Modernized architecture that is optimized for performance.
The State of Neural Machine Translation (NMT) by Philipp Koehn
Neural Machine Translation (NMT) is an exciting and promising new approach to Machine Translation. However, while the technology is promising we still…
When, Why and How to Migrate from Statistical to Neural Machine
There is a running joke in the translation industry that machine translation will be a solved problem in 5 years. This has been updated every 5 years …
Achieving an ROI from Machine Translation while Keeping Translators “Happy”
It would seem a paradox that LSP’s can achieve significant ROI from Machine Translation (MT) and keep localization staff happy. While seeking efficiencies in business right from the early days of the industrial revolution has always resulted in shifts of where human efforts are applied and these changes don’t come easy, most staff members presented with machine translation output for post-editing are frustrated mainly at the poor output they are presented with. Poor output can even go as far as for example wrong domain-specific output which will likely force an editor to rewrite significant sections of a document and negatively affect both ROI as well as staff motivation.
Riding the Machine Translation Hype Cycle – From SMT to NMT to Deep NMT by Dion Wiggins
There is a running joke in the translation industry that machine translation will be a solved problem in 5 years. This has been updated every 5 years …
Deep Neural Machine Translation
Deep Neural Machine Translation is a new technology based on Machine Learning and Artificial Intelligence (AI). It is an extension of Neural Machine Translation (NMT). Both use a large neural network with the difference that Deep NMT processes multiple neural network layers instead of just one. The result: The best machine translation quality ever experienced – and customized to your unique needs.