Slator Neural Machine Translation Report 2018

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:

Executive Summary	4
Neural is the New Black	6
The Current NMT Landscape	6
So, What Now?	7
By the End of 2017, Neural MT was Mainstream	7
Neural Network-Based Language Technology Providers	8
Current Customized NMT Deployments	10
EPO’s Patent Translate	10
Booking.com: A Trial NMT Deployment	11
NMT Performance: What NMT Can and Cannot Do	12
Exceptional Capabilities of NMT	14
Current Limitations of NMT	16
What’s Next in NMT	20
How Do You Quantify Quality?	20
Replacing BLEU.	21
Human Evaluation Remains the Ultimate Standard	21
Creating a New Quality Standard	22
New and Existing NMT QA Processes	22
Machines Testing Machines	24
Training Data Becomes Big(ger) Business	24
Publicly Accessible Corpora	25
Building Your Own Corpora	25
Buying Corpora from Others	25
Quality is Always a Caveat	26
Directions of NMT Research	27
“So Many” Exciting Research Directions	27
“Convolutional Neural Networks are Doomed”	28
Pivot Languages and Zero Shot	28
Buy Vs Build	29
Quality Versus Cost	30
Productivity and Production Boost?	31
To Build or Not To Build	32
Shifting Paradigms: Changing Models of Working	34
From the Experts	34

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?