Machine Translation: The Neural Frontier
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This was a talk given at the annual GALA conference in Amsterdam on March 27th 2017. The topic is Neural Machine Translation. Where are we now? Neural Machine Translation is at the peak of a hype cycle. There is no doubt it is an emerging technology with massive potential, but it is not yet a sweeping solution to all ills. Several factors prevent NMT from being commercially ready. Expectations, therefore, need to be managed. That is the goal of this presentation.
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Machine Translation: The Neural Frontier
- 1. Machine Translation The Neural Frontier John Tinsley GALA, Amsterdam, March 2017
- 3. What we’re actually going to cover this morning! How does it work? What’s all the fuss about? “Neural machine translation is ______.” What is the status as of today? Is it really that good? What does all this mean for the future?
- 5. What they actually said... “In some cases human and GNMT translations are nearly indistinguishable on the relatively simplistic and isolated sentences sampled from Wikipedia and news articles for this experiment.” What was reported... MT developers around the world
- 8. Source: (modified from) http://nlp.stanford.edu/projects/nmt/Luong-Cho-Manning-NMT-ACL2016-v4.pdf Rule Based Statistical Neural A brief history of MT…
- 9. “State of the Union” The initial splash made by statistical MT The initial splash made by neural MT wow that’s pretty good! We’re about here now March 27th 2007 This is where the excitement is coming from Statistical Machine Translation MTQuality Neural Machine Translation 20+ years worth of research ?
- 10. Neural machine translation is exciting!
- 11. Neural machine translation is the future
- 12. Neural machine translation is ultimately just another type of MT
- 13. Neural machine translation is not going to replace human translators
- 14. Neural machine translation is not a silver bullet
- 15. Still early stage Language independent Fundamental practical considerations not yet addressed Neural Machine Translation March 27th 2017 Generic applications only No flexibility for customisation Significant hurdles for cost- effective scalable production performance Academia Industry Output can be insanely fluent!
- 16. Source:https://www.nytimes.com/2016/12/14/magazine/the-great-ai-awakening.html They needed more computers — “G.P.U.s,” graphics processors reconfigured for neural networks — for training… “Should we ask for a thousand G.P.U.s?” “Why not 2,000?” Ten days later, they had the additional 2,000 processors.
- 17. Is it really that good? (Yes, it can be!)
- 18. • “Yeah it looks better”Anecdotal • Generally, neural is better* • More obviously so for complex languages • It falls over badly on long sentences Academic • Stark improvements for Chinese and Arabic • Comparable performance on other languages WIPO What evaluations are out there?
- 19. WIPO large scale apples-to-apples comparison English to Chinese Arabic to Chinese Spanish to Chinese French to Chinese
- 20. • “Yeah it looks better”Anecdotal • Generally, neural is better* • More obviously so for complex languages • It falls over badly on long sentences Academic • Stark improvements for Chinese and Arabic • Comparable performance on other languagesWIPO • Practical comparison with production MT • Mixed results depending on content type • Clear strengths and weaknesses emerging Iconic What evaluations are out there?
- 21. Real-world languages and content Chinese to English patents, mature production engine, highly tuned. “Real-world” comparative use case Apples to apples comparison Access to same training data, test data, including all of the ugly parts. Effective qualitative evaluation No one-size-fits-all, so what MT good and what and where does it fall down?
- 22. Short Sentences All Sentences u Iconic Production MT u Iconic Neural MT Neural MT works – and it’s good! It is not a silver bullet + word order + agreement - omitting phrases + terminology + error free output - sentence structure
- 23. New Opportunities = New Challenges Black Box Customisation Production “Why is this error happening?” “Can you fix this error please?” “How much is that GPU??!” Data Evaluation Pricing Still needed, now more than ever! Do we know how to quantify “quality”? How much does it cost now? Old Challenges
- 24. Short term • Research which takes time • More effective use of general machine translation 2-5 years • Emerging use cases, new types of hybrid, and clarity Longer term • “Zero-shot” translation? What does this mean for the future? Rule-based Statistical Neural You are here
- 25. 1st Recurrent Neural Network 2nd Recurrent Neural Network 0.034203423 3.343423423 2.234235234 0.453423423 0.002340234 2.234234234 5.023234234 3.342342355 0.034203423 3.343423423 “GO RAIBH MAITH AGAT” “THANK YOU” Encoder Decoder Encoded Sentence Gaelic Input English Output Memory of previously translated words influence next result Thank you! P.S. This is kind of how neural machine translation works… john@iconictranslation.com @johntins
Editor's Notes
- What this talk is not! => An intractable, impenetrable technical deep dive into how neural MT works!
- Fuss – context NMT is – significance / impact Status – not media / meaning for YOU Good? – examples and case studies Future – short, mid, longer term
- Before we get into it… Fake news Embellished, sensationalised, out of context reporting – not doing anyone any favours Example
- Google paper, 23 pages Not the first time History of false dawns
- I’m going to be a more friendly expectation manager! Not bursting bubble – just bringing down to earth Bombastic!
- What better way to start Answer needs historical context
- Paradigm shift Rule based didn’t go away Neural last 2 years – why the fuss?
- SMT became incremental NMT here out of the box Excitement – haven’t even had the chance to try all these things! Hype cycle
- Brand new runway Light and end of the tunnel - EXCITING
- Hard to timeframe – will try later Run in parallel But definitely the way forward
- HOWEVER still just MT + ML, like SMT Same UI, same integrations, same problems (later) But better quality = ?
- Not US vs THEM, MAN vs MACHINE Reframe conversation – competition, frustrating Complimentary technology Own use cases
- Promising, way forward, needs time… “Do you do neural machine translation?” With that being said, where are we today
- Early – fringe in 2015 General – “German nouns” Practical – not yet, glossaries, customisation BUT GOOD people Generic – no use cases Flexibility – no customisation GPUs! revisit MUCH OF IT IS A MATTER OF TIME BUT THAT’S WHERE WE STAND NOW
- Obvious question – depends on many factors, let’s look at evals
- Anecdotal – good, surface, needs deeper Academic – more effective in some cases. Room for improvement = improvement WIPO – broadly on par but some interesting. LET’S LOOK
- Automatic scores We don’t know why
- most highly optimised type of MT we want to know ourselves!
- My first experience, very cool. What are the practical implications of this? What do we do now? What’s holding us back? Now we have some direction
- Leveraging and utilising it in industry is challenging! How do we make decisions – where to use it, when, and how! Needs more practical field testing…
- “This first wave of NMT solutions are mostly generic systems, which are clearly improved in most language combinations over existing generic SMT solutions, especially to human evaluators. While we need to be wary of over exuberance about the progress, there is reason for optimism and we can expect further quality improvements as our understanding of the mystery of ‘hidden layers’ of deep learning improves”, “MT must be adaptable/customizable for specific business purposes, i.e. they need to learn specific terminology and specific customer domain. Comprehensive customization will take significantly more computing time and all the requirements for good quality data will only intensify.”