A novel approach to neural machine translation | Engineering Blog | Facebook Code

Also see Facebook posts its fast and accurate ConvNet models for machine translation on GitHub (TechCrunch)

"Language translation is important to Facebook's mission of making the world more open and connected, enabling everyone to consume posts or videos in their preferred language — all at the highest possible accuracy and speed.

Today, the Facebook Artificial Intelligence Research (FAIR) team published research results using a novel convolutional neural network (CNN) approach for language translation that achieves state-of-the-art accuracy at nine times the speed of recurrent neural systems.1 Additionally, the FAIR sequence modeling toolkit (fairseq) source code and the trained systems are available under an open source license on GitHub so that other researchers can build custom models for translation, text summarization, and other tasks."

A novel approach to neural machine translation | Engineering Blog | Facebook Code