Microsoft Translator service and API slides
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MT is useful, and it gets better and more useful when it is customized to the terminology and style of the documents to be translated. But it is extra work, not much, but extra work. In this talk you’ll get an overview of MT domain customization, its benefits, pitfalls, and conditions for making it work, as well as an overview of the actual work and helpful vs. not so helpful training documents. The theory of MT. Introduction to MT: short history, the pros and cons of different techniques. Statistical MT versus rule-based MT and what the brand new model-based MT can offer, as well as the hybridization and the challenges and possible breakthroughs.
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Microsoft Translator service and API slides
- 1. Translator
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- 4. Slide 5 •Learn word and phrase alignments from “parallel” data
- 6. Slide 7 • f e* e* = argmaxe P(e | f) • P(e | f) = P(f | e) ∙ P(e) / P(f) argmaxe P(e | f) = argmax P(f | e) ∙ P(e) •P(f | e) channel translation model •P(e) language model
- 7. Slide 8 Start With •Parallel sentences • •Monolingual data •Decoding Algorithm Build These Components •Translation Model • •Language Model – P(E) •Decoder •
- 8. Slide 9 Translation Model Target Language Model Other Models Microsoft s vast language knowledge Translation Model Target Language Model Your and your community s language knowledge Translator service and API Your Applications Your test and tuning documents Lambda weight vector
- 9. Slide 10 Your site or application Translator Service Supply Corrections Consume Translations Collaborative Translations Store Microsoft Translator Hub Custom ModelsGeneric Models Your own, previously translated documents Supply Documents Build custom models Import Corrections for training
- 10. Slide 11 Your site or application Translator Service Supply Corrections Consume Translations Collaborative Translations Store Microsoft Translator Hub Custom ModelsGeneric Models Your own, previously translated documents Supply Documents Build custom models Import Corrections for training Translate() AddTranslation() GetTranslations() GetUserTranslations() Speak() Detect() BreakSentences() Thorough customization Retrain every 2 months, or 20000 segments Continuous Improvement
- 11. Slide 12 What goes in What it does Rules to follow Be strict. Compose them to be optimally representative of what you are going to translate in the future. Calculate the BLEU score – just for you. Dictionaries Forces the given translation with a probability of 1. Be restrictive. Safe to use only for compound nouns and named entities. Better to not use and let the system learn. Build the translation model aka phrase table. Teaches how to translate. Be liberal. Any in-domain human translation is better than MT. Add and remove documents as you go and try to improve the score. Build the target language model. Improve grammar and fluency. Be liberal. Use any in-domain target language material you can get.
- 12. Slide 13 • • Humans can easily detect 0.5 to 1.0 points • Faster post-editing Higher document comprehension • • Small: Higher improvement within the domain • Large: Better suited for input variability Better exploit of training docs • Better to build a larger domain (lower BLEU delta) •
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- 14. Slide 15 Quality SpeedPrice You can only have two P3
- 15. Slide 16 Post-Editing •Goal: Human translation quality •Increase human translator’s productivity •In practice: 0% to 25% productivity increase Varies by content, style and language Raw publishing Goals: Good enough for the purpose Speed Cost Publish the output of the MT system directly to end user Best with bilingual UI Good results with technical audiences Cost-effective way for inbound material Triage Analysis and classification P3 – Post-Publish Post-Editing Know what you are human translating, and why Make use of community Domain experts Enthusiasts Employees Professional translators Best of both worlds Fast Better than raw Always current
- 16. Slide 17 Assimilation Dissemination Post-Edit Use customized machine translation Never miss a chance to collect a human edit Make the source visible on demand Show the source Show domain-relevant dictionaries Apply TM with 100% Apply TM with 80% Reveal alternatives Publish raw first, collect human feedback Use modern, collaborative TM systems (i.e. MemSource)
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- 18. Slide 19 • • • Deep Neural Networks (>30% in ASR) • Recurrent Neural Networks (1-6 BLEU) • • • Filtering, domain adaptation • • •
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Hinweis der Redaktion
- Statistical machine translation involves using a large corpus of parallel texts (in two languages) to train statistical models that are able to subsequently help translate new text. Microsoft Translator machine translation service is built on these principles, and serves millions of translations every day through various Microsoft products (Bing, Office, Internet Explorer) and a rich API.
- Slide is heavily animated.
- Slide is heavily animated.
- Although the use here demonstrates the use of the technology with deaf or hard of hearing students, it’s not much of a stretch to adapt the technology, since the components already exist, to hearing students that speak other languages. In fact, it could be used in that manner now. We haven’t tested it in this scenario…yet.