The document discusses trends in how technology is impacting localization. It explores how the explosion of online content is creating new needs for translation. It also examines different approaches to machine translation, including rule-based MT and statistical MT, and their various advantages and disadvantages. Several case studies are presented showing how companies have used machine translation and post-editing to increase translation speed, reduce costs, and improve quality and consistency.

1. From Office Machines to Machine Translation, Product Manuals to Product Blogs: How Trends in Technology Impact Trends in Localization Lori Thicke www.lexcelera.com

2. Trend: Exploding Content is Creating New Needs.

3. Trend: Multiplication of Platforms.

4. Trend: Crowdsourcing in the Cloud.

5. Trend: Increasing Globalization.

6. Trend: It’s Just Common Sense.

7. Trend: The Potential of Emerging Economies.

8. Trend: Success Through the Long Tail of Languages.

9. Trend: Products must be global and local (Glocal).

10. Trend: Companies No Longer Control Their Brand.

11. Trend: Customer Support is more virtual than ever.

12. Trend: Customers Are Doing It For Themselves.

13. Trend: Agile Isn’t Fast Enough.

14. Trend: Translate More Languages with Less.

15. Trend: Higher Quality Expectations.

16. Trend: MT has Changed the Role of Translators And of Project Managers.

19. Trend: Good Enough is Good Enough.

20. Trend: Human Translation Can’t Meet the Needs to:

21. Trend: Leveraging the Expertise of Translators.

22. .Different approaches to MT DifferentApproaches to MT Machine Translation is not one program …but different programs takingdifferentapproaches to get to the same place

23. .Different approaches to MT Rules-Based MT (RMBT) e.g. Systran, ProMT Comes with linguistic rules, trained by domain-specific user dictionaries built by linguists

24. .Different approaches to MT Statistical MT (SMT) e.g. Language Weaver, Google, Asia Online, Moses (open source) May have domain built in, trained by engineers on millions of segments

25. .Different approaches to MT Advantages of RBMT ▪Respects grammatical rules: Le chatvert ▪User dictionaries control terms ▪System can be trained/corrected in real time ▪ Large corpora not necessary ▪ Most user cases today are Systran Disadvantages of RBMT ▪Customization requires trained linguists ▪Only one language pair at a time ▪Not available for every language ▪Labour intensive to scale/extend to new domains (4-6 weeks)

26. .Different approaches to MT Advantages of SMT ▪Learns automatically ▪Same process for virtually any language ▪Customization can be done with limited human input ▪Extending to new languages/domains takes up processing capacity, not linguistic resources Disadvantages of SMT ▪Requires millions of segments of clean training text (TMs, bilingual and monolingual corpora) ▪Unpredictable: terms may be correct in one sentence and not in another ▪System makes its own decisions based on probability ▪Corrections not always easy to integrate into engine ▪Memory and processor intensive ▪No grammatical rules to govern text it hasn’t seen: le vert chat

27. Trend: Full Post-Editing for Human Quality.

28. Trend: Light Post-Editing for Information.

29. Trend: MT for Customer Support.

30. Trend: MT for Customer Support.

31. Trend: MT for Leveraging Human Translators.

32. Trend: Machine Translation Increases Speed.

33. User Study: Bentley Systems.

34. Trend: Machine Translation Reduces Costs.

35. User Study: Veolia Environment.

36. Trend: Machine Translation Improves Quality and Consistency.

37. User Study: Various Users.