Dimitar rounds off the conversation with a discussion of the findings from the evaluation project.

1. KantanNeural™ from A to Z
3/3: NMT in 4 weeks → 4 days → 4 hours
Dimitar Shterionov

2. What is NMT?
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3. What is NMT?
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x1 x2 x3 c y1 y2 y3

4. How to NMT – The Recipe
Hardware + Software:
GPUs, torch, Theano
nematus, OpenNMT
Know-how, Support
Integration,
Deployment
Training data
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5. How to NMT – KantanNeural™
Hardware + Software:
GPUs, torch, theano
nematus, OpenNMT
Know-how, Support
Integration,
Deployment
Training data
KantanNeural™
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6. KantanNeural™: black board to production
 Proof of Concept:
AWS, NVIDIA K520 GPUs
Nematus, ADAM, BPE, SCN
MT (engines) build: 4 weeks
Quality: impressive
01 Nov 2016
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• ADAM: Parameter update algorithm
• Byte-pair encoding (BPE)
• Single-character n-gram (SCN)
lower → low er
tallest → tall est
almost → al most
lowest
taller
allow

7.  KantanNeural™ α:
OpenNMT, ADAM, BPE
ΜΤ build time: 4 days
Quality: on a par with nematus
KantanFleet™
01 Nov 2016 01 Feb 2017
KantanNeural™: black board to production
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8.  KantanNeural™ β:
Build-your-own NMT
Available to all clients
(no extra charge)
Extended KantanFleet™
01 Nov 2016 01 Feb 2017 15 March 2017
KantanNeural™: black board to production
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9. 01 Nov 2016 01 Feb 2017 15 March 2017
 Currently:
Build-your-own NMT
NVIDIA K80 GPUs
AdaptiveMT
Incremental Retraining
4 hours?
30 June 2017
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KantanNeural™: black board to production

10. KantanMT.com – A Complete Platform
Build
Improve
Deploy
Kantan
Templates
Kantan
NER
Kantan
Llibrary
Kantan
Fleet
Kantan
BuildAnalytics
Kantan
Analytics
Kantan
PEX
Kantan
LQR
Adaptive
MT
Kantan
GENTRY
Kantan
TotalRecall
KantanNeural™
Kantan
Translate
Kantan
Swift
Kantan
API
Kantan
AutoScale
Kantan
OfficeMT
Kantan
Connectors
Kantan
Snippets
KantanNeural™
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11. KantanMT.com – A Complete Platform
Build Improve Deploy
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12. KantanMT.com – A Complete Platform
Build Improve Deploy
 Select a KantanFleet™ engine
 KantanFleet™ Neural (18 language
pairs)
 Multiple domains
 Create new NMT engine
 Import library data
 Import your own data
 Convert an SMT profile:
… just two clicks away from NMT
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13. KantanMT.com – A Complete Platform
Build Improve Deploy
 Select a KantanFleet™ engine
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14. KantanMT.com – A Complete Platform
Build Improve Deploy
 Create a blank KantanNeural™
engine
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15. KantanMT.com – A Complete Platform
Build Improve Deploy
 Convert a PBSMT engine into KantanNeural™
engine
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16. KantanMT.com – A Complete Platform
Build Improve Deploy
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Artificial Neural Networks train iteratively:
While stopping condition not met:
While training data not exhausted:
Take a batch
Learn from it
Repeat

17. KantanMT.com – A Complete Platform
Build Improve Deploy
Augment data
Parallel corpora
Preprocessing rules
(PEX, tokeniser excep., etc.)
F-Measure, BLEU, TER
KantanLQR
(Error typology, AB Testing)
New Preprocessing rules
New data
Augment data Augment data Aug
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18. KantanMT.com – A Complete Platform
Build Improve Deploy
Augment data
Parallel corpora
Preprocessing rules
(PEX, tokeniser excep., etc.)
F-Measure, BLEU, TER
KantanLQR
(Error typology, AB Testing)
New Preprocessing rules
New data
Augment data Augment data Aug
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19. KantanMT.com – A Complete Platform
Build Improve Deploy
Augment data
Parallel corpora
Preprocessing rules
(PEX, tokeniser excep., etc.)
F-Measure, BLEU, TER
KantanLQR
(Error typology, AB Testing)
New Preprocessing rules
New data
Augment data Augment data Aug
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4 hours?

20. KantanMT.com – A Complete Platform
Build Improve Deploy
 API
 Connectors
 KantanWidgets™
 As every other
KantanMT engine
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21. Conclusions…
 KantanMT:
 A complete MT platform for both NMT and PBSMT engines
 Easy access to powerful MT technology
 How to train, improve and deploy KantanNeural™ engines
 Seamless switch from PBSMT to NMT
 Incremental retraining to improve, adapt and specialize engines

22. Conclusions…
 KantanMT:
 A complete MT platform for both NMT and PBSMT engines
 Easy access to powerful MT technology
 How to train, improve and deploy KantanNeural™ engines
 Seamless switch from PBSMT to NMT
 Incremental retraining to improve, adapt and specialize engines
4 hours training?

23. … and future work
 Better control:
 Terminology
 Tags
 NTAs
 Learn from postedits:
 Exploit feedback from KantanLQR™
 Exploit feedback from connectors
 Models:
 Add language knowledge
 Hybrid MT
 Convolutional Neural Networks (CNN)
 …

24. Solving
Thank you…
Laura Casanellas: laurac@kantanmt.com
Dimitar Shterionov: dimitars@kantanmt.com
KantanLabs: labs@kantanmt.com
KantanMT: info@kantanmt.com