Looking Back and Leaping Forward@KantanMT info@kantanmt.com

AgendaIntroduction: Poulomi Choudhury

Marketing Team @ KantanMTSpeaker #1: Tony O’Dowd

Chief Architect, Founder & CEOLooking Back: Review of MT during 2016Leaping Forward: 5 Trends you can’t ignore

in 2017Speaker #2: Kirti Vashee

Independent ConsultantWell known MT ExpertAuthor of eMpTy Pages Blog

Q&A

Review of 2016 – Highlights

Q3 20142016=>

Fully Integrated L10 Workflows

Alignment of TM & MT

1

F.A.U.T The basic requirement for certain

industries

3

Quality Much ImprovedMultiple approaches emerge

to improve quality

4

Controlling your Destiny Many companies switch to

SAAS platforms

5

The need for Speed!MT has got a lot lot

faster!

2

Looking into the Future!

By the end of this decade, computers will disappear as distinct physical objects and AI will reach human levels

by the end of next decadeRay Kurzweil, an American scientist, inventor and futurist

Pushing the boundaries of MT

Measuring Quality will be super

important!

Online user activity and multilingual user experience

Thank You!@KantanMT info@kantanmt.com

Kirti Vasheekirti.vashee@gmail.com @kvashee

Looking Back at MT in 2016 and Leaping Forward into 2017

www.kv-emptypages.blogspot.comwww.kv-emptypages.blogspot.com

“In April 2016 Google MT does over 100 languages, and every single day they translate over 140 billion words.”

2016

Machine Translation in 2016

~ 100B

Over 500 Billion Words A Day! 2016

Neural Machine Translation Arrives

And Many Others Begin To Experiment but Ramp Up Time for NMT is Significant2016

• MT that learns in real-time so that every corrective feedback action improves the MT moment-by-moment

• The next generation approach to post-editing that works like a virtual translator assistant

• MT technology that many translators actually like

Adaptive Machine Translation Arrives

2016

THE 2017 OUTLOOK

The Market Forecast & Critical Requirements

• Giants will continue to drive technology forward for generic systems• Systran will introduce NMT domain adaptation and specialization

tools for enterprise and LSP use in Q1• Minimum 12-18 months for other MT vendors to ramp up and gain

demonstrable competence with NMT • Moses-like toolkits will emerge but will be very complex and need

expensive computing resources and deep knowledge• More surrounding analysis and diagnostic support tools will emerge

to build NMT eco-system• Could eventually replace Phrase-Based SMT (3-5 years)

Neural Machine Translation Evolves and Builds Momentum

2017

• For 2017 AMT systems will likely outperform all other approaches in output quality in most LPs when active corrective feedback is provided

• SDL could be a formidable AMT competitor to Lilt if they are able to properly integrate TM & AMT technology

• Technology continues to evolve in capability and likely to become the dominant Do-It-Yourself part of the MT market

Adaptive Machine Translation Evolves

2017

PB-SMT will remain

dominant MT model for

2017

• MQM and TAUS DQF can be useful but are too complex , slow and expensive to use on a regular basis

• Accurate MT output quality assessment is critical to getting PEMT compensation right. We need a trusted Effort Score!

• Accurate assessment also allows better predictability on overall project outcomes

• BLEU is marginally useful with NMT and maybe not even relevant with Adaptive MT

• New measures needed but MUST be Quick, Cheap and Easily Implementable

MT Output Quality Measurement Will Improve Beyond BLEU and TER

2017

• PEMT Compensation needs to be transparent and closely linked to correction efforts i.e. more correction = more compensation

• Proper compensation can make an MT project successful and improper compensation can cause failure even with good engines

• Current practices are too arbitrary and not based on good and robust engine-specific quality data

• Dire need for a quick and relatively accurate MT output quality assessment method, that can be widely used as a standardized approach to determine compensation

• Analyze multiple metrics against actual PEMT effort over time across many projects to develop clear guidelines and validate metrics

• An opportunity for industry collaboration to build a standard.

Post Editing Compensation Standardization Urgently Needed

2017

Kirti Vashee – kirti.vashee@gmail.com

Follow Me on Twitter: @kvashee

Join the Automated Language Translation Group in LinkedIn

www.kv-emptypages.blogspot.com

Thank You