transLectures project presentation for the Internet of Education 2013 (Ljubljana, Slovenia, 11-12 Nov 2013). Prepared and delivered by Gonçal Garcés Díaz-Munío. Watch this presentation in video: http://videolectures.net/internetofeducation2013_diaz_munio_translectures/ Same presentation slides in the transLectures Slideshare: http://www.slideshare.net/transLectures/trans-lectures-ioe201320131112 IoE 2013: http://www.k4all.org/event/1st-internet-of-education-conference/ The transLectures project (2011-2014): http://www.translectures.eu

1. Watch this presentation in video:
http://videolectures.net/internetofeducation2013_diaz_munio_translectures/
Internet of Education 2013
12 November 2013
Universitat Politècnica de València
www.translectures.eu
EC FP7 ICT project #287755

2. Outline
• transLectures (tL): motivation and goals
• Video demo
• tL technologies
• Progress and results
• Scientific evaluations
• User evaluations
• Quality control (expert evaluations)
• Implementation and integration
• tL open source tools
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3. Motivation
• Video lecture repositories and MOOCs
• Thousands of hours of video lectures available
• Hundreds of hours of video lectures recorded
every week
• Most video lectures only available in their
original language
• No subtitles
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4. Motivation
• Transcriptions and translations are needed
•
•
•
•
•
Accessibility for people with disabilities
Accessibility for speakers of different languages
Search and analysis functions
Automated topic finding
…
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5. Motivation
• Transcriptions and translations are needed
•
•
•
•
•
Accessibility for people with disabilities
Accessibility for speakers of different languages
Search and analysis functions
Automated topic finding
…
• How do we get there?
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6. The transLectures approach
1. Automatic Speech Recognition (ASR)
and Machine Translation (MT)
• Adaptation: Taking advantage of the characteristics
of video lecture repositories
• High-quality automatic transcriptions and translations
2. Interactive postediting:
intelligent interaction for reduced effort
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7. Goals
• Massive adaptation
• Intelligent interaction
• Implementation
• Case studies: Videolectures.NET & Polimedia
• Real-life evaluation
• Integration into Opencast Matterhorn
http://opencast.org/matterhorn/
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8. The transLectures partners
1
2
Name
Universitat Politècnica de València
Xerox SAS
Country
Spain
France
3
3+
4
Institut Jožef Stefan
Knowledge for All Foundation
RWTH Aachen University
Slovenia
UK
Germany
5
6
EML – European Media Laboratory
DDS – Deluxe Digital Studios
Germany
UK
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9. Languages
• Transcription (ASR)
• EN
• SL
• ES
• Translation (MT)
•
•
•
•
EN>SL , SL>EN
EN>ES , ES>EN
EN>FR
EN>DE
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10. transLectures: video demo
http://www.translectures.eu/new-demo-video/
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11. Massive adaptation
• Characteristics
of video lectures
Just one person
Known speaker
Clear talking
No interruptions
Focused on a topic
Slides
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12. Massive adaptation
• Known speaker and topic
• Slides
• Related documents
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13. Scientific evaluations (Y2)
• Transcription results
Worse
• WER: Word Error Rate (%)
• Goal: WER < 25%
• EN, SL, ES
Better
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14. Scientific evaluations (Y2)
• Translation results
Better
• BLEU
• Goal: BLEU > 30
•
•
•
•
EN>SL , SL>EN
EN>ES , ES>EN
EN>FR
EN>DE
Worse
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15. Y1 results and comparison
Org. X = Undisclosed, state of
the art ASR & MT systems
Org. X
Org. X
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16. Y1 results and comparison
Org. X = Undisclosed, state of
the art ASR & MT systems
Org. X
Org. X
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Org. X
Org. X
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17. Intelligent interaction
• Postediting automatic transcriptions/translations
• The user invests the least possible effort
• The system learns the most from it
• Confidence measures
• Fast constrained search
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18. Intelligent interaction
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19. Intelligent interaction
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20. Intelligent interaction
• User evaluations
• UPV (Polimedia)
• JSI (Videolectures.NET)
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21. User evaluations
• User evaluations at UPV
• Users: lecturers
• Revising their own lectures
• 3 different experiments
1. Complete supervision
2. Intelligent interaction
3. Two-round supervision
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22. User evaluations
1. Complete supervision
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23. User evaluations
2. Intelligent interaction
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24. User evaluations
3. Two-round supervision
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25. User evaluations
• User evaluations at UPV: results
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26. User evaluations
• User evaluations at UPV: results
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27. User evaluations
• User evaluations at UPV: results
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28. Quality control: expert evaluations
• Transcription quality (EN, ES, SL)
• UPV: Representative set of Spanish transcriptions
• Avg. WER: 23.2 ; Avg. RTF: 3.8
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29. Quality control: expert evaluations
• Transcription quality (EN, ES, SL)
• UPV: Representative set of Spanish transcriptions
• Avg. WER: 23.2 ; Avg. RTF: 3.8
• Translation quality (EN<>SL, EN<>ES, EN>FR, EN>DE)
• UPV: Representative set of Spanish>English translations
• Avg. BLEU: 46.6 ; Avg. RTF: 14.8 ; Avg. Score: 3.6 out of 5
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30. Implementation and integration
• Videolectures.NET
• Polimedia
• Opencast Matterhorn
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31. • Polimedia
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32. transLectures: Open source tools
• The tL player (& editor)
• Coming soon (www.translectures.eu)
• The transLectures-UPV Toolkit (TLK) for ASR
• www.translectures.eu/tlk
• RWTH Aachen: rASR, Jane (MT)
• http://www-i6.informatik.rwth-aachen.de/web/Software/
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33. Next steps for transLectures
• Keep improving ASR and MT results
• Keep improving tL open source tools (TLK, tL player)
• External user evaluations (VL.NET and polimedia)
• External trials: implementation in other universities
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34. • More detailed info in the public project deliverables,
soon available from
http://www.translectures.eu/progress/
http://www.translectures.eu/public-reports/
(M12 = Year 1; M24 = Year 2, most recent results)
• More tL video demos:
http://www.translectures.eu/progress/
• Follow transLectures:
• http://twitter.com/translectures
• http://www.facebook.com/translectures
• http://www.slideshare.net/transLectures
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35. www.translectures.eu
• About this presentation:
Gonçal Garcés Díaz-Munío
• Project coordinator:
Alfons Juan-Ciscar
ggarces@dsic.upv.es
ajuan@dsic.upv.es
EC FP7 ICT Programme – Project Number 287755
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