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...

Recently, I had the opportunity and kind invitation to attend the SYSTRAN community day event where many members of their product development, marketing, and management team gathered with major customers and partners. The objective was to share information about the continuing evolution of their new Pure Neural MT (PNMT) technology,  share detailed PNMT output quality evaluation results, and provide initial customer user experience data with the new technology. Also, naturally such an event creates a more active and intense dialogue between company employees and customers and partners.  This, I think has substantial value for a company that seeks to align product offerings with its customer's actual needs. Ongoing Enhancements of the PNMT Product Offering The event made it clear that SYSTRAN is well down the NMT path, possibly years ahead of other MT vendors, and provided a review of the current status of their rapidly evolving PNMT technology. Some highlig...

SYSTRAN announced earlier this week that they are doing a “first release” of their Pure Neural™ MT technology for 30 language pairs. Given how good the Korean samples that I saw were, I am curious why Korean is not one of the languages that they chose to release. "Let’s be clear, this innovative technology will not replace human translators. Nor does it produce translation which is almost indistinguishable from human translation"  ...  SYSTRAN BLOG The languages pairs being initially released are 18 in and out of English, specifically EN<>AR, PT-BR, NL, DE, FR, IT, RU, ZH, ES  and 12 in and out of French  FR<>AR, PT-BR, DE, IT, ES, NL. They claim these systems are the culmination of over 50,000 hours of GPU trainings but are very careful to say that they are still experimenting and tuning these systems and that they will adjust them as they find ways to make them better. They have also enrolled ten major customers in a beta program to validate the ...

This is the second part of an interview with Jean Senellart (JAS) , Global CTO and SYSTRAN SAS Director General. The first part can be found here: A Deep Dive into SYSTRAN’s Neural Machine Translation (NMT) Technology . Those translators who accuse MT vendors of stealing or undermining their jobs should take note that SYSTRAN is the largest independent MT vendor. A position it has held for most of its existence, but has never generated more than €20 million in annual revenue. Which to me suggests that MT is mostly being used for different kinds of  translation tasks and is hardly taking any jobs away. The problem has been much more related to unscrupulous or incompetent LSPs who used MT improperly in rate negotiations. MT is hugely successful for those companies who find other ways to monetize the technology, as I pointed out in  The Larger Context Translation Market .This does suggest that MT has huge value in enabling global communication and commerce, and even in its les...

One of the wonderful things about my current independent status is the ability to engage deeply with other MT experts who were previously off limits because competing MT vendors don't usually chat with open hearts and open cloaks. MT is tough to do well and I think the heavy lifting should be left to people who are committed for the long run, and who are willing to play, invest and experiment in spite of regular failure. This is how humans who endure and persist, learn and solve complex problems. This is Part 1 of a two-part post on the SYSTRAN NMT product announcement. The second part will focus on comparing NMT with RBMT and SMT and also with the latest Adaptive MT initiatives. It can be found here: Comparing Neural MT, SMT and RBMT – The SYSTRAN Perspective Press releases are so filled with marketing-speak as to be completely useless to most of us. They have a lot of words but after you read them you realize you really don't know much more than you got from the headline. S...