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.

Advertisement

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 Mainstream7
  • 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 NMT20
  • 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 Build29
  • 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?

Click the “Buy Now” button below to get your copy.

Best purchased in combination with the Slator 2019 Neural Machine Translation Report—Deploying NMT in Operations published in December 2018

Slator Neural Machine Translation Report 2018

Slator reports
35-page report. Looks at current state and business case for NMT with expert commentary from over a dozen industry experts and leading academic researchers.
$48 BUY NOW

Slator

Slator makes business sense of the language services and technology industry with news on the people and deals that shape the industry.