It's a fact. More and more organizations are exploring the benefits of machine translation. Some people believe automated translation will be ubiquitous; part of every system we use to interact with content — tablets, smart phones, mobile devices, computer kiosks, bank machines, consumer electronics, appliances, automobiles, trains, buses and planes. But, is our content ready for machines to process? Can automated translation systems produce the quality we desire? And, if so, what do we need to do to prepare it? Attend this session to learn how thinking strategically about your content can help you make smart decisions up front that will provide big benefits downstream. Learn how creating structured, semantically-rich content today can help you feed automated translation systems tomorrow.

1. Thinking Strategically
About Content
Destined for Machine
Translation
Val Swisher
Founder & CEO
@contentrulesinc
© 2013. Content Rules, Inc. All rights reserved.

2. Who Am I?
 Founder and CEO of Content Rules
 25+ years in content arena
 Specialty areas:
 Global content strategy
 Terminology management
 Content quality
 Single-sourcing / XML / DITA
 Finishing third book, “Global Content Strategy,” due out in
2014
© 2013. Content Rules, Inc. All rights reserved.

3. What is Content Rules?
 Professional services firm specializing in:
• Content strategy / Global content strategy
• Content creation
• Content quality / Global readiness
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Based in Silicon Valley
Founded in 1994
Acrolinx Authorized Services Provider
Authorized provider of The Rockley Strategic Method™
© 2013. Content Rules, Inc. All rights reserved.

4. Global Readiness
 Ensure content is translatable
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Readability
Grammar and style
Reuse
 Evaluate and improve content quality using state-of-the-art tools
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Reports
Metrics
Recommendations
Fixes
 Save money on translation
© 2013. Content Rules, Inc. All rights reserved.

5. © 2013. Content Rules, Inc. All rights reserved.

6. Today’s Presentation
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Importance of content
Historic background
Types of machine translation
Content quality affects machine translation results
Bleu scores
Pre-editing instead of post-editing
© 2013. Content Rules, Inc. All rights reserved.

7. Content Is Important
87% of respondents to a recent CMO Council survey said that content had
a moderate to major impact on their buying decisions
© 2013. Content Rules, Inc. All rights reserved.

8. Content Is A Strategic Asset
© 2013.
Content Rules, Inc.
All rights reserved.

9. What Does It Mean to be Strategic?
stra·te·gic
[struh-tee-jik]
adjective
1. pertaining to, characterized by, or of the
nature of strategy: strategic movements.
2. important in or essential to strategy.
3. forming an integral part of a stratagem:
a strategic move in a game of chess.
© 2013.
Content Rules, Inc.
All rights reserved.

10. Content Creation In the Past
 Content wasn't so easy to create and distribute
 Created by trained professionals
 Only they had access to the content
© 2013.
Content Rules, Inc.
All rights reserved.

11. Content Creation Today
 Everyone creates content
 Very easy to distribute
 Now, we have loads and loads of content
• Some of it good
• Some of it mediocre
• Some of it downright awful
© 2013.
Content Rules, Inc.
All rights reserved.

12. Translation In The Past
 Content wasn't so easy to translate.
 Trained professionals
 Only they understood multiple
languages well enough to translate
content
© 2013.
Content Rules, Inc.
All rights reserved.

13. Translation Today
 It is easy and free to translate content
 We have loads and loads of translated content
•
Some of it good
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Some of it mediocre
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Some of it downright awful
© 2013.
Content Rules, Inc.
All rights reserved.

14. More Machine Translation All The Time
 Machine Translation (MT) is becoming more relied upon as a way to
get cost-effective, fast translations
 %18.05 year-over-year growth of MT expected over next 3 years*
 Must pay a more attention to the source content that goes into it
 A machine cannot figure what we meant to say based on what we
actually wrote
 Garbage In – Garbage Out
*http://www.researchandmarkets.com/research/2gpj3p/global_machine
© 2013.
Content Rules, Inc.
All rights reserved.

15. Source Content And Machine Translation
 Types of MT engines and the effect of source content
on them
 What are Bleu scores
 How quality of content affects MT output
© 2013.
Content Rules, Inc.
All rights reserved.

16. MT Engine Types
There are three types of MT Engines:
1. Rule-based
2. Statistical
3. Hybrid
© 2013.
Content Rules, Inc.
All rights reserved.

17. Rule-Based MT (RBMT)
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Uses linguistic rules
Extensive use of bilingual dictionaries
Transfers structure of source language into target language
Results are literal translations based on rules
Does not handle ambiguity well (word or phrase having more than
one meaning)
© 2013.
Content Rules, Inc.
All rights reserved.

18. Statistical MT (SMT)
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Based on analysis of content
Engine trained over time
More content = better results
Need at least 2,000,000 million words per domain
Better quality content = better results
Results are more natural translations, based on previous source |
destination pairs
 Google Translate
© 2013.
Content Rules, Inc.
All rights reserved.

19. Hybrid
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Combines rule-base and statistical
Provides predictability and consistency of RBMT
Provides fluency and flexibility of SMT
Reduces the amount of data needed to train the engine
© 2013.
Content Rules, Inc.
All rights reserved.

20. Training The SMT Beast
 Training SMT software extremely important
 Poor quality source = Poor quality translations
 Some companies have such poorly trained MT engines
that fixing the content first is actually not an option
 The engine has been trained to translate poor quality
source
© 2013.
Content Rules, Inc.
All rights reserved.

21. The Effect Of Poor Content On SMT And
Hybrid MT
 Poor or unpredictable translations
 Increased time to retrain the system with correct
information
 Increased post-editing, per language
 Wasted money
© 2013.
Content Rules, Inc.
All rights reserved.

22. Evaluating MT Precision - Bleu Scores
 Introduced in 2002 by the IBM Watson Research Center
 Automatic evaluation metric used to compare MT output
with reference human translation
“The closer a machine translation is to a professional human translation, the
better it is.” *
 Metric widely used throughout the industry
*http://acl.ldc.upenn.edu/P/P02/P02-1040.pdf
© 2013.
Content Rules, Inc.
All rights reserved.

23. Bleu Scores – Helpful Or Hype?
According to Callison-Burch, Osborne, and Koehn of the School on
Informatics, University of Edinburgh, Bleu scores have many issues*:
 Synonyms and paraphrases difficult to score
 All words are weighted equally
 Difficult to calculate
*http://homepages.inf.ed.ac.uk/pkoehn/publications/bleu2006.pdf
© 2013.
Content Rules, Inc.
All rights reserved.

24. That’s Okay. We Can Post Edit.
Original
Source
Content
Post-Edited
Translations
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Content Rules, Inc.
All rights reserved.

25. Why Not Pre-Edit Instead?
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Fewer issues = less post editing
Save time
Save money
Improve quality
© 2013.
Content Rules, Inc.
All rights reserved.

26. Create Global-Ready Content
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Reduce word count
Standardize terminology
Enforce correct grammar
Eliminate jargon and colloquialisms
Increase reuse
© 2013.
Content Rules, Inc.
All rights reserved.

27. Results of Pre-Editing
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Save money
Improve quality
Faster time to market
Fewer in-country iterations
Better translation consistency
© 2013.
Content Rules, Inc.
All rights reserved.

28. Summary
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Content is a strategic asset
Machine translation is becoming more popular
Poor quality content incorrectly trains MT engines
Poor quality content results in increased post-editing
Pre-editing saves money and time, and improves
translation quality
© 2013.
Content Rules, Inc.
All rights reserved.

29. Val Swisher
vals@contentrules.com
@contentrulesinc
© 2013.
Content Rules, Inc.
All rights reserved.

30. Val Swisher
CEO & Founder
vals@contentrules.com
@contentrulesinc

31. © 2013.
Content Rules, Inc.
All rights reserved.

32. Reduce word count
 We recommend 24 words, max, for machine translation.
 It is impossible for people to understand long sentences.
Imagine software having to parse through all of those
commas (half of which are probably missing or misplaced).
© 2013.
Content Rules, Inc.
All rights reserved.

33. Let's say we have 100,000 words of source content.
We are going to translate the content into 14 languages.
We will end up with 1.4 million words of content.
Let's say the 100,000 words contain all types of errors. We will have to post-edit and fix
1.4 million words on the other side.
Let's say we have to pay someone <<<$ .xx>>> per word to post-edit the content.
That's <<<$.xx>>> * 1,400,000 words.
If we paid <<<$ .07>>> per word to predit the content, we would have spent $7,000 for
preparation.
© 2013.
Content Rules, Inc.
All rights reserved.