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© Pactera. Confidential. All Rights Reserved. 1
Balancing Translation Time and Accuracy
for Video Content
Xiao Yuan (Pactera Technology Limited)
© Pactera. Confidential. All Rights Reserved. 2
 A daily average of 2
hours on watching
online video per user
 YouTube has more
than 1 billion videos
with machine-
translated captions
Can Machine Translation Solve All Problems?
 Minimize the cost and
the time in fixing
machine translation
errors
Industry Trend Challenges
An Example of “Bad” Machine Translation (1)
© Pactera. Confidential. All Rights Reserved. 3
An Example of “Bad” Machine Translation (2)
© Pactera. Confidential. All Rights Reserved. 4
How To Choose The Right Translation?
© Pactera. Confidential. All Rights Reserved. 5
Pactera
translators
Crowdsourced
feedback
Machine
translation
Pactera judging
system
• Based on our PactWork
crowdsourcing platform
• Text analysis
© Pactera. Confidential. All Rights Reserved. 6
What We Have Achieved So Far
© Pactera. Confidential. All Rights Reserved. 7
Technology
Integration
6000+
Hours of Videos
200+
Languages
20000+ language
Specialists
Conclusion
© Pactera. Confidential. All Rights Reserved. 8
We estimate to help customers save more than 50% of
costs in translation
While machine translation can quickly translate a large
amount of online video content, it is still not perfect
Pactera provides crowdsourcing platform to improve the
machine translation accuracy from 60% to > 90%
Quality
Cost
Accuracy
Xiao Yuan
Localization Director China Division
xiao.yuan@pactera.com
WeChat ID: 18910015818

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Achieving Translation Efficiency and Accuracy for Video Content, Xiao Yuan (Pactera)

  • 1. © Pactera. Confidential. All Rights Reserved. 1 Balancing Translation Time and Accuracy for Video Content Xiao Yuan (Pactera Technology Limited)
  • 2. © Pactera. Confidential. All Rights Reserved. 2  A daily average of 2 hours on watching online video per user  YouTube has more than 1 billion videos with machine- translated captions Can Machine Translation Solve All Problems?  Minimize the cost and the time in fixing machine translation errors Industry Trend Challenges
  • 3. An Example of “Bad” Machine Translation (1) © Pactera. Confidential. All Rights Reserved. 3
  • 4. An Example of “Bad” Machine Translation (2) © Pactera. Confidential. All Rights Reserved. 4
  • 5. How To Choose The Right Translation? © Pactera. Confidential. All Rights Reserved. 5 Pactera translators Crowdsourced feedback Machine translation Pactera judging system • Based on our PactWork crowdsourcing platform • Text analysis
  • 6. © Pactera. Confidential. All Rights Reserved. 6
  • 7. What We Have Achieved So Far © Pactera. Confidential. All Rights Reserved. 7 Technology Integration 6000+ Hours of Videos 200+ Languages 20000+ language Specialists
  • 8. Conclusion © Pactera. Confidential. All Rights Reserved. 8 We estimate to help customers save more than 50% of costs in translation While machine translation can quickly translate a large amount of online video content, it is still not perfect Pactera provides crowdsourcing platform to improve the machine translation accuracy from 60% to > 90% Quality Cost Accuracy
  • 9. Xiao Yuan Localization Director China Division xiao.yuan@pactera.com WeChat ID: 18910015818

Notas do Editor

  1. 互联网行业趋势:越来越多的信息通过互联网传达给用户。 根据研究表明,全球平均每一个人一天花在观看互联网视频上的时间是2小时。这给了视频提供者、视频网站等巨大的商机。 当然,为了获取全球用户,Youtube率先使用语音识别加机器翻译的技术,让全球用户可以通过机器翻译观看视频,以此扩大他们的市场。 即使如此,在Youtube用机器翻译完了100万个视频以后,他们的机器也并未表现的很完美。下面请看一些他们的问题。
  2. 举例子
  3. 虽然其中会有错误和其他各种噪声,但随着时间的推移,它会产生一种非常真实的、人性化的文化沟通桥梁。