Webinar: Looking Back at 2016 and Leaping Forward with Translation in 2017
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Transcript of Webinar: Looking Back at 2016 and Leaping Forward with Translation in 2017
Looking Back and Leaping Forward@KantanMT [email protected]
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 [email protected]
Kirti [email protected] @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 – [email protected]
Follow Me on Twitter: @kvashee
Join the Automated Language Translation Group in LinkedIn
www.kv-emptypages.blogspot.com
Thank You
