TechGist05

This is the final post for the 2017 year, a guest post by Jean Senellart who has been a serious MT practitioner for around 40 years, with deep expertise in all the technology paradigms that have been used to do machine translation. SYSTRAN has recently been running tests building MT systems with different datasets and parameters to evaluate how data and parameter variation affect MT output quality. As Jean said: " We are continuously feeding data to a collection of models with different parameters – and at each iteration, we change the parameters. We have systems that are being evaluated in this setup for about 2 months and we see that they continue to learn." This is more of a vision statement about the future evolution of this (MT) technology, where they continue to learn and improve, rather than a direct reporting of experimental results, and I think is a fitting way to end the year in this blog. It is very clear to most of us that deep learning based approaches are the wa...

2017 was an especially big year for the Neural MT momentum on multiple fronts. We saw DeepL and Amazon introduce new generic NMT product offerings, each with a unique twist of their own. They are both impressive introductions but are limited to a small set of language pairs, and both these companies have made a big deal about superior MT quality on using some limited tests to document this and attract attention. For those who define better quality by BLEU scores, these new offerings do indeed have slightly higher scores than other generic MT solutions. But for those who wish to deploy an industrial scale MT solution, there are other things that matter more e.g. the extent of customization possibilities and the range of steering possibilities available to update and tune the base engine, the overall build platform capabilities, and the ability to secure and maintain data privacy are particularly important. The Asian market also saw several NMT initiatives build momentum, especially with...