Neural is the new black in language technology. Google, Microsoft, IBM, SAP, Systran, Facebook, Amazon, Salesforce, Baidu, Sogou, SDL, DeepL—and this is the short version of a much longer list that includes Iconic, KantanMT, Omniscien, Lilt, Globalese, and TransPerfect (via Tauyou) and many other startup and mid-sized players that have become involved in neural machine translation (NMT).
Some of them offer NMT solutions. Some use proprietary systems for unique problems. Others are researching ways to incorporate NMT into their existing service and product lines.
By now, the generic praise heaped upon the new technology is becoming repetitive: it outperforms statistical machine translation (SMT), it is a genuine breakthrough in AI tech, and it is fast-paced in terms of research and deployment.
The industry is well past discussing the emergence of NMT. Clearly, neural is the new black. Now the main concern is to see if you look good in black.
NMT in 2018
Slator’s Neural Machine Translation Report 2018 looks at the current state of NMT from several angles that make it clear what the business use case is for the now-ubiquitous but still-developing technology.
Supported by expert commentary from over a dozen industry experts and leading academic researchers, this report includes:
|Neural is the New Black||6|
|By the End of 2017, Neural MT was Mainstream||7|
|What’s Next in NMT||20|
|Buy Vs Build||29|
While undoubtedly mainstream by now, NMT still raises many questions both from the buy and sell side.
What can NMT actually do? What existing deployments make the case for NMT in production environments? Are there any downsides to the technology? What does it take to build your own system? Who are the technology vendors in the space? What happens to existing structures, technology, and working paradigms now that NMT is becoming the new standard?
Best purchased in combination with the Slator 2019 Neural Machine Translation Report—Deploying NMT in Operations published in December 2018