Get Started with KantanNeural

1. Get Started with KantanNeuralTM Tony O’Dowd Dimitar Shterionov

2. Agenda  Introduction to KantanMT  Introduction to KantanNeural™  SMT  NMT  What’s the difference?  KantanLQR  A-B Testing to measure translation quality  Q&A

3.  Statistical MT Platform  Cloud-based  Highly scalable for high volume  Fully Automated Useable Translation (FAUT)  Fusion of TM & MT & rules  Multiple Deployment Options  MT Factory for Rapid Development  Our Vision  To put Machine Translation  Customization  Improvement  Deployment  into your hands What is KantanMT.com? Active KantanMT Engines 10,394 Training Words Uploaded 534,179,753,587 Member Words Translated 8,571,269,925 www.kantanMT.com

4. Service Breakdown 0.7 1.3 2.4 1.9 Q1 Q2 Q3 Q4 Words Translated (in Billions) Total Number of words translated per quarter FY 2016 Sectoral Breakdown eCommerce Online Retailing, Product Catalogues Technology Software, User Assistance Materials, Web sites Others Support, Legal, Medical and other sectors

5. The KantanMT Community Some clients un-named for

6. Academic Partner Program

7. Industry Affiliations & Memberships

8. KantanMT.com – A Complete MT Platform Kantan Templates Kantan NER Kantan Llibrary Kantan Fleet Kantan BuildAnalytics Build Kantan Analytics Kantan PEX Kantan LQR Adaptive MT Kantan GENTRY Kantan TotalRecall Kantan Neural Improve Kantan Translate Kantan Swift Kantan API Kantan AutoScale Kantan OfficeMT Kantan Connectors Kantan Snippets Deploy

9. KantanMT.com – A Complete MT Platform Kantan Templates Kantan NER Kantan Llibrary Kantan Fleet Kantan BuildAnalytics Build Kantan Analytics Kantan PEX Kantan LQR Adaptive MT Kantan GENTRY Kantan TotalRecall Improve Kantan Translate Kantan Swift Kantan API Kantan AutoScale Kantan OfficeMT Kantan Connectors Kantan Snippets Deploy Kantan Neural KantanNeural™ • Customisable • Fast • High quality

10. Learning to translate from data  (Phrase-based) Statistical Machine Translation  Phrase table: I did→hebe ich I did→ich hebe Unfortunately→leider Unfortunately→unglücklich Receive an asnwer→emfange eine Antwort Receive an answer→Antwort emfange Receive an answer→Antwort bekommen Receive an answer→Antwort erhalten I did not unfortunately receive an answer to this question Auf diese Frage hebe ich leider keine Antwort bekommen

11. Learning to translate from data  (Phrase-based) Statistical Machine Translation  A large phrase-based dictionary  Based on smart mathematics and sophisticated tools  Requires human intervention  High demand for better translation  SMT has reached a point that cannot respond to the demand  Neural MT (NMT)

12. Learning to translate from data… NMT  Recurrent Neural Networks  Have memory – compressing input sequences  No more phrases – sequences of vector-represented words  Encode-decoder  A source sentence encoded as a vector c  The decoder predicts a word based on c and the already predicted words.

13. Learning to translate from data… NMT  Parallel data to train NMT model  Jointly learns to encode and decode  Words are represented as vectors (1-Hot encoding) x1 x2 x3 c y1 y2 y3

14. Learning to translate from data… NMT  Large vocabulary → large vectors to deal with  Unknown words  SMT → do not translate  NMT → Unexpected behaviour  Word segmentation  Byte-pair encoding  lower → low er  tallest → tall est  almost → al most  Single character n-gram:  Hundreds of thousands of words in English but 26 characters. How about Simplified Chinese? Japanese? lowest taller allow

15. KantanNeural™ - under the hood  Vector manipulations are very computationally expensive  GPU Machines with K520 Grid  Training, translation, API architecture  Modular → Easy to upgrade and migrate  Distributed → Highly efficient  GPU and CPU  Word segmentation  Single character n-gram for DBC  Byte-pair encoding for SBC

16. KantanNeural™ - under the hood  Considered tools  theano-based (nematus)  torch-based (OpenNMT)  OpenNMT  5x faster than nematus  Large development community

17. KantanNeural™ - current state  Yesterday  Fast, customizable, high-quality SMT engines  Today  KantanFleet™ charged with KantanNeural™  Tomorrow  Customizable KantanNeural™ engines  TM, Terminology, NER  Fast training on prebuilt models  Expanded fleet of GPU powered machines

18. KantanNeural™ - Deployment  3 Language Combinations  EN-DE, EN-ZH, EN-JP  Now available for use on the KantanMT Platform  Beta I Release  Part of the KantanFleet Collection of pre-built engines  KantanMT Account holders can now translate  All document formats are supported  New Language Arcs will be added during Q1 2017 Arcs # Segments # Words Domain EN-DE 8.8 million 156 million Legal EN-ZH 3.5 million 53 million Legal EN-JA 8.1 million 90 million Legal

19. KantanNeural™ - identification

20. KantanNeural™ - select from Fleet

21. KantanNeural™ - set the engine type

22. But how about Quality?

23. KantanNeural™ - Quality  Phase 1 : Automated Test Score Comparisons Arcs Type F-Measure BLEU TER EN-DE SMT 68% 59% 50% NMT 67% 49% 51% Arcs Type F-Measure BLEU TER EN-ZH SMT 76% 43% 45% NMT 73% 43% 44% Arcs Type F-Measure BLEU TER EN-JA SMT 78% 53% 45% NMT 68% 40% 53%

24. KantanNeural™ - Quality  A reliable quality measurement not available at present  SMT higher BLEU  Human translators prefer NMT output Arcs Type F-Measure BLEU TER EN-DE SMT 68% 59% 50% NMT 67% 49% 51% Arcs Type F-Measure BLEU TER EN-JA SMT 78% 53% 45% NMT 68% 40% 53% Arcs Type F-Measure BLEU TER EN-ZH SMT 76% 43% 45% NMT 73% 43% 44% NMT SMT Ref. Diese Bandbreite stellt somit eher eine Überbewertung der Produktion dar. Es handelt sich daher eher um eine zu hoch angesetzte Produktionsschätzung. Es handelt sich daher eher um eine zu hoch angesetzte Produktionsschätzung. This range therefore represents an overestimation of production. It is therefore a question of a too high estimation of production. It is therefore a question of a too high estimation of production.

25. KantanNeural™ - Quality  A reliable quality measurement not available at present  SMT higher BLEU  Human translators prefer NMT output Arcs Type F-Measure BLEU TER EN-DE SMT 68% 59% 50% NMT 67% 49% 51% Arcs Type F-Measure BLEU TER EN-JA SMT 78% 53% 45% NMT 68% 40% 53% Arcs Type F-Measure BLEU TER EN-ZH SMT 76% 43% 45% NMT 73% 43% 44% NMT SMT Ref. Diese Bandbreite stellt somit eher eine Überbewertung der Produktion dar. Es handelt sich daher eher um eine zu hoch angesetzte Produktionsschätzung. Es handelt sich daher eher um eine zu hoch angesetzte Produktionsschätzung. This range therefore represents an overestimation of production. It is therefore a question of a too high estimation of production. It is therefore a question of a too high estimation of production. Therefore this range represents rather an overestimate of the production.

26. Translation Quality Evaluation

27. KantanLQR  Quick Introduction to KantanLQR  Built into the KantanMT platform  Integral step in KantanMT Engine Development

28. KantanLQR  Project Types now supported:-  Quality Evaluation  Factored Model  KPIs based on  Simplified Factors  MQM  DQF  Harmonised MQM-DQF  Real-time dashboard  A/B Test  Comparison Model  KPIs based on  A-B  A-B-C and  A-B-C-D  Real-time dashboard

29. KantanLQR

30. KantanLQR

31. KantanLQR

32. KantanLQR

33. KantanLQR

34. KantanLQR

35. KantanLQR

36. KantanLQR

37. KantanLQR

38. Conclusions

39. Conclusions  KantanNeural™  Available in three language arcs from KantanFleet™  Efficient training with OpenNMT and K520 Grid GPUs  Inherits the merits of KantanMT pipelines  A/B Testing  A, B (C or D) testing now fully supported  Real-time data analytics built into your LQR Dashboard  Both KantanNeural™ and A/B Testing are available to all KantanMT Account holders

40. Future work  KantanNeural™  Build-your-own KantanNeural™ engine  Incremental/adaptive NMT  API support for NMT  Further distribute the training and translation pipelines for even higher efficiency  Intelligent data preprocessing for even faster and more efficient training.  A/B Testing

41. Solving Thank you…

42. Solving Thank you…

43. Translation Quality Evaluation  KantanLQR  Built into the KantanMT platform  Integral step in KantanMT Engine Development  Translation Quality Evaluation  Factored Model  Templates based on Simplified Factors, MQM, and DQF and MQM-DQF  A/B Testing  A, B (C or D) testing now fully supported  Real-time data analytics built into your LQR Dashboard  Available to all KantanMT Account holders

44. KantanLQR A/B Testing  Comparative analysis between two, three or four different Machine Translation (MT) outputs.  Designed to compare SMT with NMT outputs

45. KantanLQR A/B Testing - create

46. KantanLQR A/B Testing - create

47. KantanLQR A/B Testing – add data

50. KantanLQR A/B Testing – scoring

51. KantanLQR A/B Testing – review

52. KantanLQR A/B Testing – review

Get Started with KantanNeural

Get Started with KantanNeural

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