Author: Dr. Patrik Lambert, Machine Translation Scientist @ Iconic“Garbage in, Garbage out” - noisy data is a big problem for all machine learning tasks, and MT is no different. By noisy data, we mean bad alignments, poor translations, misspellings, and other inconsistencies in the data used to train the systems. Statistical MT systems are more robust, and can cope with up to 10% noise in...
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Author: Dr. Rohit Gupta, Sr. Machine Translation Scientist @ Iconic Training a neural machine translation engine is a time consuming task. It typically takes a number of days or even weeks, when running powerful GPUs. Reducing this time is a priority of any neural MT developer. In this post we explore a recent work (Ott et al, 2018), whereby, without compromising the translation quality, they...
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The field of Machine Translation is moving at as fast a pace as we've ever seen. Month on month, there is an increase in the number of research papers being published, with the majority obviously focusing on Neural MT. As a company at the forefront of this technology, it's critically important that we at Iconic stay up to date. Our world-class team of MT research scientists...
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