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META-NET has received funding from the EU’s Horizon 2020 research and innovation programme through the contract CRACKER
(g...
Language Technology
q Text and Content Analytics (NLP, Multimedia Processing)
§ Information Extraction, Relation Extract...
Outline
q Initiatives for Multilingual Europe
q Towards the Multilingual Digital Single Market
q MDSM SRIA V0.9
q Next...
q
60 research centres in 34 countries (founded in 2010)
Chair of Executive Board: Jan Hajic (CUNI)
Dep.: J. van Genabith ...
1 DFKI Germany Georg Rehm
2 CUNI Czech Republic Jan Hajic
3 ELDA France Khalid Choukri
4 FBK Italy Marcello Federico
5 ATH...
http://www.cracker-project.eu • http://www.meta-net.eu
• Riga Summit 2015 and Riga Declaration.
• Federation of European p...
q Top priority in the European Union.
q Expected to add 400b€ to European GDP
and hundreds of thousands of new jobs.
q ...
http://www.meta-net.eu 10
Facts and Figures
http://www.meta-net.eu 11
THREE PRIORITY AREAS FOR ACHIEVINGTHE MULTILINGUAL DIGITAL SINGLE MARKET
Multi...
A. Ansip’s May 2016 Blog Post
q Posted on 27 May 2016.
q First public acknowledgment
of the EC that the language
topic i...
13
Language as a Data Type
q Language technology is a necessary ingredient of the multilingual
DSM and mandatory enabler for...
q Overall goal: “deliver new Big
Data technology allowing for deep
analytics capacities on data-at-
rest and data-in-moti...
BDVA SRIA V2.0: Challenges and Needs
q BDVA SRIA Technical Priority “Data Management”:
§ Tools for handling unstructured...
MDSM SRIA
q Version 0.5 unveiled at META-FORUM 2015
q Version 0.9 unveiled at META-FORUM 2016
q Version 1.0 foreseen fo...
Strategic Research and Innovation Agenda
Language as a Data Type and
Key Challenge for Big Data
Enabling the Multilingual ...
MLV Programme
q Multilingual Value Programe*
§ Three-year programme
§ Requires modest investment
q “Enabling the Multi...
Multilingual Digital Single Market
Automated Translation
E-Commerce
Content, Media,
Verticals
Translation, Language,
Knowl...
MDSM: Goals and Needs
q Crosslingual communication for SMEs, public institutions, citizens
q Crosslingual SME presales c...
Application Areas (Selection)
q Multilingual E-commerce
§ Customer-facing vs. back-office facing (after-market, after-sa...
Three Phases: 2018–2020
q Pre-MLV (2016/2017): stakeholder discussions and consensus building; finalisation of
strategy a...
Setup – Timeframe
q Close collaboration with EC, EP and all other stakeholders
(including SMEs, research centres, univers...
Next Steps
a) extend Cracking the Language Barrier federation (BDVA?);
b) discuss MDSM SRIA V0.9 with BDVA: what are BDVA’...
Thank you for your attention.
georg.rehm@dfki.de
http://www.meta-net.eu
http://www.facebook.com/META.Alliance
http://www.c...
Language Technologies for Big Data – A Strategic Agenda for the Multilingual Digital Single Market.
Language Technologies for Big Data – A Strategic Agenda for the Multilingual Digital Single Market.
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Language Technologies for Big Data – A Strategic Agenda for the Multilingual Digital Single Market.

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Georg Rehm. Language Technologies for Big Data – A Strategic Agenda for the Multilingual Digital Single Market. BDVA Summit (Big Data Value Association), Valencia, Spain, December 2016. December 1, 2016.

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Language Technologies for Big Data – A Strategic Agenda for the Multilingual Digital Single Market.

  1. 1. META-NET has received funding from the EU’s Horizon 2020 research and innovation programme through the contract CRACKER (grant agreement no.: 645357). Formerly co-funded by FP7 and ICT PSP through the contracts T4ME (grant agreement no.: 249119), CESAR (grant agreement no.: 271022), METANET4U (grant agreement no.: 270893) and META-NORD (grant agreement no.: 270899). Language Technologies for Big Data: A Strategic Agenda for the Multilingual Digital Single Market Georg Rehm Coordinator CRACKER, General Secretary META-NET georg.rehm@dfki.de BDVA Summit – Valencia, Spain, 1st December 2016
  2. 2. Language Technology q Text and Content Analytics (NLP, Multimedia Processing) § Information Extraction, Relation Extraction, Knowledge Extraction § Mining of: Topics, Sentiments, Opinions, Arguments, Rumours etc. § Goal: From Data to Knowledge (Smart Data) – no deep NLU yet q Semantic Technologies – Knowledge Technologies § Semantic Web, semantic descriptions, ontologies, reasoning § RDF, Linked Data, Knowledge Graphs, grounding q Multilingual and Crosslingual Technologies (Machine Translation) § Technologies to bridge language barriers q Text and Report Generation – From data to text q Curation Technologies – mix of tech to create and curate content q Conversational Interaction Technologies (ASR, DS, TTS etc.) q Multilingual Europe – Technologies for all European languages http://www.meta-net.eu 2
  3. 3. Outline q Initiatives for Multilingual Europe q Towards the Multilingual Digital Single Market q MDSM SRIA V0.9 q Next Steps http://www.meta-net.eu – http://www.cracker-project.eu 3
  4. 4. q 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) q 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 4
  5. 5. 1 DFKI Germany Georg Rehm 2 CUNI Czech Republic Jan Hajic 3 ELDA France Khalid Choukri 4 FBK Italy Marcello Federico 5 ATHENA RC Greece Stelios Piperidis 6 UEDIN UK Philipp Koehn 7 USFD UK Lucia Specia Coordination and Support Action, H2020-ICT17, 2015–2017, 36 months – http://www.cracker-project.eu Cracking the Language Barrier Coordination, Evaluation and Resources for European MT Research THREE PRIORITY AREAS FOR ACHIEVINGTHE MULTILINGUAL DIGITAL SINGLE MARKET Multilingual access to all digital goods and services across Europe1 Geo-blocking: due to nationality, location, or residence Language-blocking: languages they do not speak Geo-blocking and language-blocking are barriers to access Customers are six times more likely to buy from sites in their native language. Most EU languages address less than 3% of the market, fundamentally limiting SMEs operating in countries where those languages are spoken. Lack of language technology support (automatic translation, tools to assist human translators, and multilingual support in European businesses. Language can be expensive for SMEs Online businesses face around €5,000 in up-front costs for each new language they translate their websites into, plus similar and marketing costs. Even when sites are translated, the vast majority of SMEs cannot respond to support requests or customer feedback in other languages. Such responsiveness is needed to achieve customer satisfaction and build brand loyalty. English is not the answer 52% of EU customers do not purchase Adding even a few languages to an SME’s website beyond English can have a major impact on revenue. Large organizations today to increase market share. 6x more likely to purchase Site in buyer’s native language Site in foreign language Likelihoodofpurchasing THREE PRIORITY AREAS FOR ACHIEVINGTHE MULTILINGUAL DIGITAL SINGLE MARKET Multilingual access to all digital goods and services across Europe1 Geo-blocking: due to nationality, location, or residence customers Language-blocking: languages they do not speak however, current online translation is insufficient trying to conduct common languages Geo-blocking and language-blocking are barriers to access Both geo-blocking and language-blocking are daily problems for tens of millions of EU citizens. Customers are six times more likely to buy from sites in their native language. Most EU languages address less than 3% of the market, fundamentally limiting SMEs operating in countries where those languages are spoken. Lack of language technology support (automatic translation, tools to assist human translators, and multilingual support in European businesses. Language can be expensive for SMEs Online businesses face around €5,000 in up-front costs for each new language they translate their websites into, plus similar and marketing costs. Even when sites are translated, the vast majority of SMEs cannot respond to support requests or customer feedback in other languages. Such responsiveness is needed to achieve customer satisfaction and build brand loyalty. English is not the answer 52% of EU customers do not purchase Adding even a few languages to an SME’s website beyond English can have a major impact on revenue. Large organizations today to increase market share. 6x more likely to purchase Site in buyer’s native language Site in foreign language Likelihoodofpurchasing Communities • META-NET incl. META-SHARE and META • MT evaluation initiatives – WMT, IWSLT, MT Marathons • MT and other LT industry • Language resources – META-SHARE, ELRA • HT/MT evaluation tools – translate5 • Translation industry, translation profession • MT user communities Strategic Agenda for the Multilingual Digital Single Market • Version 0.5 presented at META-FORUM 2015 (Riga) • Version 0.9 presented at META-FORUM 2016 (Lisbon) Strategic Research and Innovation Agenda Language as a Data Type and Key Challenge for Big Data Enabling the Multilingual Digital Single Market through technologies for translating, analysing, processing and curating natural language content SRIA Editorial Team Version 0.9 – July 2016 5
  6. 6. http://www.cracker-project.eu • http://www.meta-net.eu • Riga Summit 2015 and Riga Declaration. • Federation of European projects and organisations working on technologies for a multilingual Europe. • Multi-lateral Memorandum of Understanding; 10 organisations and 24 projects on board. • Getting new members on a regular basis. • Selected areas of collaboration: data management and repositories, tools, shared tasks, evaluations, events. • Goal: provide one umbrella organisation for the whole community.
  7. 7. q Top priority in the European Union. q Expected to add 400b€ to European GDP and hundreds of thousands of new jobs. q Unfortunately, the language topic is not included in the EC’s Digital Single Market strategy (published in May 2015). 7
  8. 8. http://www.meta-net.eu 10
  9. 9. Facts and Figures http://www.meta-net.eu 11 THREE PRIORITY AREAS FOR ACHIEVINGTHE MULTILINGUAL DIGITAL SINGLE MARKET Multilingual access to all digital goods and services across Europe1 Customers are six times more likely to buy from sites in their native language. Most EU languages address less than 3% of the market, fundamentally limiting SMEs operating in countries where those languages are spoken. Language can be expensive for SMEs Online businesses face around €5,000 in up-front costs for each new language they translate their websites into, plus similar Even when sites are translated, the vast majority of SMEs cannot respond to support requests or customer feedback in other languages. Such English is not the answer 52% of EU customers do not purchase Adding even a few languages to an SME’s website beyond English can have a major impact on revenue. Large organizations today to increase market share. 6x more likely to purchase Site in buyer’s native language Site in foreign language Likelihoodofpurchasing
  10. 10. A. Ansip’s May 2016 Blog Post q Posted on 27 May 2016. q First public acknowledgment of the EC that the language topic is of very high relevance for the Digital Single Market. q “Overcoming language barriers is vital for building the DSM, which is by definition multilingual. It is now time to reduce and remove the language barriers that are holding back its advance, and turn them into competitive advantages.” http://www.meta-net.eu – http://www.cracker-project.eu 12
  11. 11. 13
  12. 12. Language as a Data Type q Language technology is a necessary ingredient of the multilingual DSM and mandatory enabler for the European data economy. q Big Data is never only numerical – there’s always a language component: unstructured text content, column heads, metadata etc. q Without language technology, Big Data analytics won’t happen. q The EU Data Economy needs Multilingual Big Data Content Analytics and Multilingual Big Data Content Generation. http://www.meta-net.eu 14 Unstructured data Language Technology Structured data Heterogeneous data Homogeneous data Big data Knowledge Unorganised data Organised data Multilingual big data Crosslingual analytics
  13. 13. q Overall goal: “deliver new Big Data technology allowing for deep analytics capacities on data-at- rest and data-in-motion while providing sufficient privacy guarantees, optimized user experience support and a sound data engineering framework.” q “In Europe, text-based data resources occur in many different languages […].” q “This multilingualism of data sources makes it often impossible to use existing tools and to align available resources, because they are generally provided only in the English language.” q “Thus, the seamless aligning of data sources for data analysis or business intelligence applications is hindered by the lack of language support and availability of appropriate resources.” (p. 23) http://www.meta-net.eu 15
  14. 14. BDVA SRIA V2.0: Challenges and Needs q BDVA SRIA Technical Priority “Data Management”: § Tools for handling unstructured and semi-structured data for different languages. § Annotation frameworks for integration of annotation technologies and data formats. § Techniques for semantic interoperability such as standardised data models and interoperable architectures for different sectors. § Standards and multilingual knowledge repositories that allow the seamless linking of data. q BDVA SRIA Technical Priority “Data Analytics”: § Improved, more accurate statistical models, especially with regard to semantic analysis. § Deep learning, contextualisation, machine learning, NLP, smart data analytics and real- time semantic analysis, including event and pattern discovery. § Methods for unstructured multimedia analytics and data mining, linking algorithms to deliver cross-domain and cross-sector intelligence. q BDVA SRIA Technical Priority “Data Processing”: § Real-time analytics and event processing of highly heterogeneous data sources and formats § Processing, linking, aligning data sets with one another, including semantic representations, unstructured, semi-structured and structured data, and multimedia data etc. § Knowledge extraction out of heterogeneous data sets. § Special emphasis on quality, precision, robustness http://www.meta-net.eu 16
  15. 15. MDSM SRIA q Version 0.5 unveiled at META-FORUM 2015 q Version 0.9 unveiled at META-FORUM 2016 q Version 1.0 foreseen for early 2017 q Prepared and presented by Cracking the Language Barrier federation (editorial team: 13 colleagues) q SRIA addresses how the LT community is going to act united in order to make the DSM multilingual q Aligned to three of the BDVA SRIA V2.0’s technical priorities: Data Management, Data Analysis, Data Processing. D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT D RAFT Strategic Agenda for the Multilingual Digital Single Market Technologies for Overcoming Language Barriers towards a truly integrated European Online Market D RAFT Version 0.5 – April 22, 2015 http://www.meta-net.eu – http://www.cracker-project.eu 17
  16. 16. Strategic Research and Innovation Agenda Language as a Data Type and Key Challenge for Big Data Enabling the Multilingual Digital Single Market through technologies for translating, analysing, processing and curating natural language content SRIA Editorial Team Version 0.9 – July 2016 http://www.cracker-project.eu http://www.cracking-the-language-barrier.eu
  17. 17. MLV Programme q Multilingual Value Programe* § Three-year programme § Requires modest investment q “Enabling the Multilingual Digital Single Market through technologies for translating, analysing, processing and curating natural language content” q Three components address the main needs of the Multilingual DSM (MDSM) and how to put them into practice: 1. Multilingual Application Areas 2. Multilingual Services 3. Research http://www.meta-net.eu – http://www.cracker-project.eu 19 Strategic Research and Innovation Agenda Language as a Data Type and Key Challenge for Big Data Enabling the Multilingual Digital Single Market through technologies for translating, analysing, processing and curating natural language content SRIA Editorial Team Version 0.9 – July 2016 * SRIA V0.9 and MLV Programme devised before re-organisation of DG CONNECT.
  18. 18. Multilingual Digital Single Market Automated Translation E-Commerce Content, Media, Verticals Translation, Language, Knowledge, Data Knowledge and Data Repositories Multilingual Applications Multilingual Services Research Crosslingual Big Data Language Analytics Meaning, Semantics, Knowledge High-Quality Machine Translation SMEs CEF DSIs IT Integrators Research provide innovative applications fills gaps H2020 RIAs H2020 CSAs, IAs, RIAs H2020 CSAs, RAs, national funding Multimodal Interaction Language Processing, Analysis and Production – Language Resources Citizens Public Business interoperable and standardised collaboration with member states Conversational Technologies Strategic Research and Innovation Agenda Language as a Data Type and Key Challenge for Big Data Enabling the Multilingual Digital Single Market through technologies for translating, analysing, processing and curating natural language content SRIA Editorial Team Version 0.9 – July 2016 MLV Programme
  19. 19. MDSM: Goals and Needs q Crosslingual communication for SMEs, public institutions, citizens q Crosslingual SME presales communication and aftersales services q Multilingual (big) data, language and knowledge value chains q Multilingual websites, product catalogues, product descriptions q Multilingual knowledge bases and knowledge graphs (and services) q Multilingual conversational interfaces for connected devices (IoT) q Crosslingual business intelligence (e.g., based on UGC) q Crosslingual social media analytics for EU-wide societal issues q Multilingual text and report generation (knowledge/data to text) q All services must be domain-adaptable (no one size fits all) q Translation Centre (Cloud) – HQ automated translation for all http://www.meta-net.eu – http://www.cracker-project.eu 21
  20. 20. Application Areas (Selection) q Multilingual E-commerce § Customer-facing vs. back-office facing (after-market, after-sales) § Crosslingual search, CRM, helpdesks, processes, workflows § Semantic, crosslingual product descriptions and catalogues § Online dispute resolution q Multilingual Content, Media, Verticals § Content analytics, curation, generation (incl. authoring support) § Multimodal communication (conversational, written, IoT) § Vertical domains: health, government, mobility, energy, legal. q Translation, Language, Knowledge, Data § Translation Cloud – written/spoken, automatic/human § Crosslingual public and social intelligence, business intelligence § HQ resources, under-resourced languages, domain-specific LRs 22
  21. 21. Three Phases: 2018–2020 q Pre-MLV (2016/2017): stakeholder discussions and consensus building; finalisation of strategy and roadmap; selection and prioritisation of topics; small prototype projects for proofs of concept; CSAs for planning, coordination, support, community building. q MLV Phase 1 (2018): first Multilingual Services; conceptualise Multilingual Applications; integration of services into applications; start and continue activities in the priority research themes q MLV Phase 2 (2019): extension of Multilingual Services (coverage, quality, precision); standardisation activities; deployment of first applications; business models; continue research activities q MLV Phase 3 (2020): extension of Multilingual Services (incl. standardisation); deployment of Multilingual Applications; transformation of projects into sustainable entities; continue research q Post-MLV (2021+): Scaling up and extending Multilingual Applications and Multilingual Services; expanding language and domain coverage; going beyond Europe, penetrating other markets; exploration of novel research strands etc. http://www.meta-net.eu 23
  22. 22. Setup – Timeframe q Close collaboration with EC, EP and all other stakeholders (including SMEs, research centres, universities, NGOs etc.). q Mix of funding sources: § Horizon 2020 (WP 2018-2020) for EU projects (RA, RIA, CSA) § National/regional funding sources: - Production of monolingual technologies and data sets - Support and grow SMEs in this area q Timeframe 2018, 2019, 2020: § Includes set of mission-critical services and applications http://www.meta-net.eu – http://www.cracker-project.eu 24
  23. 23. Next Steps a) extend Cracking the Language Barrier federation (BDVA?); b) discuss MDSM SRIA V0.9 with BDVA: what are BDVA’s concrete requirements and priorities with regard to LT? c) specify concrete set of research results and services; prioritise needed applications, services, research areas; d) MDSM SRIA V1.0 to be finalised in Q1 2017; http://www.meta-net.eu 25 Language Technology for Big Data Applications Multilingual Digital Single Market
  24. 24. Thank you for your attention. georg.rehm@dfki.de http://www.meta-net.eu http://www.facebook.com/META.Alliance http://www.cracker-project.eu http://www.cracking-the-language-barrier.eu 26

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