KantanFest: Tony O'Dowd
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Tony (Chief Architect, KantanMT.com) opens the proceedings with a temporal look at how MT technology has progressed. While embracing Rule Based MT in the 1970s, the industry switched over to Statistical MT around 2002 and is now faced with a new paradigm of Neural MT in 2016. For each technology progression, improved translation quality and fluency were achieved. Summary: https://www.youtube.com/watch?v=19yyDa6mAsc Full video: https://www.youtube.com/watch?v=EtbML0DTNHk
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- 2. Solving
- 4. 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
- 5. The KantanMT Community Some clients un-named for
- 6. KantanMT.com – A Complete Platform Kantan Templates Kantan NER Kantan Llibrary Kantan Fleet Kantan BuildAnalytics Build Kantan Analytics Kantan PEX Kantan LQR Adaptive MT Kantan GENTRY Kantan TotalRecall KantanNeural Improve Kantan Translate Kantan Swift Kantan API Kantan AutoScale Kantan OfficeMT Kantan Connectors Kantan Snippets Deploy
- 7. The Evolution of KantanMT Kantan BuildAnalytics: Helping engineers build better MT Q3 2014 Kantan Analytics: 1st Predictive Quality Estimation Technology. www.kantanmt.com: 1st SMT Cloud Based Platform (TotalRecall) KantanLQR: Making translation evaluation easy. Confirmed Scale: 8.6 billion words translated, 1/3 trillion training words KantanFleet: 250 domain-trained Domain Ready-to-Go MT. KantanNeural: Embracing the future of MT today
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- 10. Technology Progression Phrase-Based Statistical MT 1970 2002 2016
- 11. Technology Progression Neural MT 1970 2002 2016
- 12. Neural Networks • Sequence to sequence (seq-2-seq) Models • Recurrent Neural Network (RNNs) • Attention-based uni-directional RNNs • Sequences represented as Vectors
- 13. The Fundamentals of Vectors Neural Networks Represent words as vectors (ie distinct numbers) Distribution Analysis of words across corpus – context! Semantically similar words will be mapped to nearby points
- 14. The Fundamentals of Vectors Using a larger corpus yields interesting results University – College Information – Data Extending this to sequences yields very interesting results This is at the core of Neural MT Dimitar will Deep-Dive into this later
- 15. How do we know? 37 21 13 24 10 21 24 21 34 19 28 25.2 37 58 53 56 62 53.2 ENGLISH->CHINESE ENGLISH->JAPANESE ENGLISH->GERMAN ENGLISH->ITALIAN ENGLISH->SPANISH ALL A/B Ranking SMT – NMT Ranked by Professional Translators Same SMT NMT
- 16. Speakers
- 17. Speakers Separating the Hype from the Reality: Neural Machine Translation
- 18. Speakers KantanMT : The User Perspective. Have you got the right stuff?
- 19. Speakers KantanNeural – The evolution of Neural MT at KantanResearchLabs Validating KantanNeural in an industrial setting
- 20. Agenda
- 21. Let’s Begin!
Editor's Notes
- We are the fastest growing MT provider, even though we are one of the young-guns!