Georg Rehm. Towards a Human Language Project for Multilingual Europe: AI and Interpretation. DG Interpretation Conference - Interpretation: Sharing Knowledge & Fostering Communities. European Commission, Brussels, April 2018. April 19/20, 2018. Invited talk.
Spring Boot vs Quarkus the ultimate battle - DevoxxUK
Towards a Human Language Project for Multilingual Europe: AI and Interpretation
1. Georg Rehm
German Research Center for Artificial Intelligence (DFKI) GmbH
Language Technology Lab – Berlin, Germany
META-NET, General Secretary
georg.rehm@dfki.de
Towards a Human Language
Project for Multilingual Europe
AI and Interpretation
4. SCIC Universities Conference (19/20 April 2018)
Data Intelligence
Current breakthroughs based on Machine Learning (“Deep Learning”)
Also still in use: symbolic, rule-based methods and systems
Artificial Intelligence
• Huge data sets + powerful algorithms + extremely fast hardware
• Enormous potential for disruptions in all sectors and areas
4
6. • Multilingualism is at the heart of the European idea
• 24 EU languages – all have the same status
• Dozens of regional and minority languages as well as
languages of immigrants and trade partners
• Many economic and social challenges:
– The Digital Single Market needs to be multilingual
– Cross-border, cross-lingual, cross-cultural
communication
7. !
60 research centres in 34 countries (founded in 2010)
Chair of Executive Board: Jan Hajic (CUNI)
Dep.: J. van Genabith (DFKI), A. Vasiljevs (Tilde)
General Secretary: Georg Rehm (DFKI)
!
Multilingual Europe
Technology Alliance.
826 members in
67 countries
(published in 2013) (31 volumes; published in 2012)
T4ME (META-NET) CESAR METANET4UMETA-NORDMultilingual Europe Technology AllianceNET
15. • Since approx. 2015, with breakthroughs in neural technolo-
gies, Machine Translation has been getting better and better.
• All areas of AI look for “super-human performance” but
language is fundamentally different and much more complex.
• Neural AI approaches cannot understand language, they
process it according to huge underlying data sets.
• In many use cases, mistakes can be tolerated.
• But: translation and interpretation are often mission-critical!
• Mistakes can have serious consequences (politics, medicine).
Translation and Interpretation
SCIC Universities Conference (19/20 April 2018) 15
16. • Example: Lecture Translator
– University lectures are automatically transcribed and translated,
in near-real time, into several languages
– Students can follow the translation through a web interface
• Example: Presentation Translator
– Presenter can have the speech automatically translated
– Translations are displayed as subtitles
• Example: Call Translator
– Internet telephony provider offers automatic voice translation
Speech Translation
SCIC Universities Conference (19/20 April 2018) 16
17. • The three example applications work surprisingly well for
general-domain language and input. But:
– They are far from being perfect.
– They aren’t robust.
– They cannot cope with unforeseen situations.
– They cannot understand language as humans do.
– They are not (yet?) suited for conference interpretation.
! Limitations as regards their fields of application.
• Interpretation is often mission-critical.
! Human interpreters won’t be replaced anytime soon.
Issues and Limitations
SCIC Universities Conference (19/20 April 2018) 17
18. SCIC Universities Conference (19/20 April 2018) 18
https://slator.com/features/ai-interpreter-fail-at-china-summit-sparks-debate-about-future-of-profession/
20. • LT in Europe: World class research, strong SME base, thousands
of LSPs; immense fragmentation; need for coordination.
• Need for High-Quality LT: translation, interpretation, MDSM etc.
• The European Language Challenge cannot be – it must not be –
abandoned or outsourced!
! Need for Language Technology, made in Europe, for Europe!
! STOA Workshop in the EP (January 2017): “Language equality in
the digital age – towards a Human Language Project”
LT – Current Developments
SCIC Universities Conference (19/20 April 2018) 20
STUDY
EPRS | European Parliamentary Research Service
Scientific Foresight Unit (STOA)
PE 581.621
Science and Technology Options Assessment
21. • Goal: Deep Natural Language Understanding by 2030
• Vision: EU FET Flagship Project (10+ years)
• Broad coverage, high quality, high precision
• Create approaches, algorithms, data sets, resources
• Across modalities: text, text types, speech, video etc.
Artificial Intelligence
including cognition, perception, vision,
cross-modal, cross-platform, cross-culture etc.
Machine Learning
Language TechnologyLinguistics
SCIC Universities Conference (19/20 April 2018)
Human Language Project
21
22. Summary & Conclusions
• AI is disrupting all industries – including translation
and, increasingly, also interpretation.
! But: perfect, robust, precise language technologies (incl.
written/spoken MT and interpretation) are still far away.
• Linguists are increasingly needed – new profiles emerging
! The machine will support human experts and help them
become more efficient – it will not replace them.
• The Human Language Project is still a vision. Its goal:
develop new breakthroughs in Language Technology.
SCIC Universities Conference (19/20 April 2018) 22
23. Recommendation
• SCIC Speech Repository
• 4,000 speeches (3,000 public + 1,000 private)
• Extremely interesting data set and language resource for
Language Technology researchers!
• Many R&D groups currently work on TED talk data sets
• Recommendation: establish bridges between SCIC
and research groups for spoken language translation
• Help build the next generation of AI tools for interpreters
• AI tools that are tailored to the needs and wishes, topics
and domains of conference interpreters in the EC/EP
SCIC Universities Conference (19/20 April 2018) 23
24. Thank you!
Dr. Georg Rehm
DFKI Berlin
georg.rehm@dfki.de
http://de.linkedin.com/in/georgrehm
https://www.slideshare.net/georgrehm
SCIC Universities Conference (19/20 April 2018) 24
Strategic Research and Innovation Agenda
Language Technologies for
Multilingual Europe
Towards a Human Language Project
SRIA Editorial Team
Version 1.0 – December 2017