TAUS OPEN SOURCE MACHINE TRANSLATION SHOWCASE, Seattle, Full Service Enterprise-Specific MT for Global Enterprises, Alon Lavie, Safaba, 17 October 2012
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This presentation is a part of the MosesCore project that encourages the development and usage of open source machine translation tools, notably the Moses statistical MT toolkit. MosesCore is supported by the European Commission Grant Number 288487 under the 7th Framework Programme. For the latest updates, follow us on Twitter - #MosesCore
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TAUS OPEN SOURCE MACHINE TRANSLATION SHOWCASE, Seattle, Full Service Enterprise-Specific MT for Global Enterprises, Alon Lavie, Safaba, 17 October 2012
- 1. TAUS OPEN SOURCE MACHINE TRANSLATION SHOWCASE Full-Service Enterprise-Specific Machine Translation for Global Enterprises 10:50-11:10 Wednesday, 17 October Alon Lavie Safaba Translation Solutions
- 2. SAFABA Enterprise Machine Translation October 17 | 2012
- 3. The Machine Translation Revolution “Translation of text by a computer that learned how to translate from vast amounts of previously translated text” • Provides volume & velocity to Translation • Integrated into existing translation process Customer Understanding Experience the Market • Enables real-time Translation MT • Embedded into business processes Internal Discovery Communication s • Rapid implementation • And gets better with time 3
- 4. BUT, Machine Translation is Limited Standard Machine Translation No real Extremely modeling of data syntax sensitive • Limited handling of • NOT for localization “Good enough” for language translation but Reacts poorly to data morphological markers / dilution punctuation / sentence structure 4
- 5. Safaba Overcame MT Deficiencies • Innovative Technology | New Process • IntroducingOptimization Engine /Module (T-LTM) > Target Language Engine (Moses+)Module (S-LTM) Core Translation Transformation Language Source LanguageTransformation > Rule-based statisticalMT Language Optimization Safaba’s Enhanced post-processing Proprietarystatistical engine post-editing pre-processing Technology™ optimization optimization > Publication language data optimization Corporate/domain language SMT source • Not a hybrid… 5
- 6. Modularity Multi- domain Beyond Localization Quality support by design Nuan ced divisi onal langu age on single syste m No disru ption of indivi dual divisi on opera tions 6
- 8. New Business Model > A three-way partnership with common infrastructure Real time Language translation aaS Language technologies Technologies services Cloud User User Real time Post edited translation aaS translation Buyer User TMS Language Premium language Services Post edited and inter-cultural Providers translation services 8
- 9. Thank You!
Notas do Editor
- Translation accuracy, domain relevance and brand consistency in MT determine attainable volume and velocity!To localize successfully MT’s role is to provide volume & velocity(high-value & high-volume content) Human raw reviewed MTThe higher the MT output quality, the greater the volume & velocity achieved in both scenarios Hence our focus… standard SMT is simply not good enough!Requires massive data manipulation and suffers from unsupervised learning.So we made it better!Easily said, not so easily done! Requires deep understanding of existing and emerging technologies We designed a modular approach to enterprise-optimized MTIt requires a business model that caters for the benefits and limitations of MT today – Professional Services suiteWe deliver MT as a Service (unless otherwise required by client)We provide full customization, adaptation, implementation and post implementation services using proprietary tools and leveraging expert domain knowledge.Taking a ‘do-it-yourself’ approach to Machine Translation has proven not to deliver the quality necessary and hence not to deliver the ROI expected.
- Translation accuracy, domain relevance and brand consistency in MT determine attainable volume and velocity!To localize successfully MT’s role is to provide volume & velocity(high-value & high-volume content) Human raw reviewed MTThe higher the MT output quality, the greater the volume & velocity achieved in both scenarios Hence our focus… standard SMT is simply not good enough!Requires massive data manipulation and suffers from unsupervised learning.So we made it better!Easily said, not so easily done! Requires deep understanding of existing and emerging technologies We designed a modular approach to enterprise-optimized MTIt requires a business model that caters for the benefits and limitations of MT today – Professional Services suiteWe deliver MT as a Service (unless otherwise required by client)We provide full customization, adaptation, implementation and post implementation services using proprietary tools and leveraging expert domain knowledge.Taking a ‘do-it-yourself’ approach to Machine Translation has proven not to deliver the quality necessary and hence not to deliver the ROI expected.
- Optimization is not Moses (no need to mention Moses based)Do not be specific re optimization technology, what elements were replaced or even avoid using ‘target language’ Stress that target language transformation Standard SMT requires massive data manipulation and suffers from unsupervised learningA multi-phase process is required to achieving highly-tuned, enterprise-specific results.Fine tuning three parameters constantly:Translation accuracy – Natural language source and target transformation and adapted (bilingual) translationDomain relevance – Enterprise and domain specific target language (monolingual) optimizationBrand consistency – Modular approach enables near real-time learning from errors and constant improvement
- Standard SMT requires massive data manipulation and suffers from unsupervised learningA multi-phase process is required to achieving highly-tuned, enterprise-specific results.Fine tuning three parameters constantly:Translation accuracy – Natural language source and target transformation and adapted (bilingual) translationDomain relevance – Enterprise and domain specific target language (monolingual) optimizationBrand consistency – Modular approach enables near real-time learning from errors and constant improvement