2. Constantin Orasan (UoW) EXPERT Introduction


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2. Constantin Orasan (UoW) EXPERT Introduction

  1. 1. Introduction to EXPERT Constantin Orasan University of Wolverhampton, UK
  2. 2. Structure       What are Marie (Skłodowska) Curie ITN actions? The EXPERT project Objectives of the project Work packages Individual projects Consortium
  3. 3. What are Marie Curie ITN actions?  Initial Training Networks (ITN):  Offer the early-stage researchers the opportunity to improve their research skills  Join established research teams  Enhance their career prospects  Are run by consortia made up of universities, research centres and companies  Recruit of researchers who are in the first five years of their career for initial training – for a research-level degree (PhD or equivalent) or be doing initial post-doctoral research.
  4. 4. EXPERT: EXPloiting Empirical appRoaches to Translation  proposes  the creation of an Initial Training Network to train young researchers on ways to improve current data-driven MT technologies (TM, SMT and EBMT)  support young researchers of the network during the whole research and development cycle, providing guidance, core and complementary training skills and evaluating the resulting technologies  young researchers to become future leaders in this area
  5. 5. EXPERT  Advocates there is no clear boundary between fully automatic and semi-automatic translation and that they are tools that can help human translators  Aims to:  improve existing corpus-based TM and MT technologies  create hybrid technologies  exploit the strengths of the existing technologies and address their main limitations  consider the needs of the users when proposing new technologies
  6. 6. Training objectives  EXPERT has five main Training Objectives:  Training through research based on the set of sub-programmes  Creating a large and diverse research community focused on a common goal.  Exploiting intersectoral and transnational mobility via secondments and shorter visits to both industrial and academic partners.  Local training in core research and complementary skills within both academic and industrial environments.  Network-wide training in core research areas and complementary skills.
  7. 7. Objectives of the project Topic State-of-the-art and limitations EXPERT solutions User perspective MT systems force the users to change their working style. Consider the real needs of translators, involving them in the development of technologies, and providing training to prepare them with new skills. Data collection and preparation Existing TM, EBMT and SMT approaches have particular data constraints. Investigate how data repositories can be built automatically in a way that makes them useful to multiple corpus-based approaches to translation.
  8. 8. Objectives of the project (2) Topic State-of-the-art and limitations EXPERT solutions Improve matching and retrieval with linguistic processing Lack of linguistic processing constrains for the retrieval of previous translation. Investigate matching algorithms which rely on lexical, syntactic and semantic variations of texts, including the use of automatically acquired domain ontologies and terminology databases Hybrid approaches for translation Hybrid corpus-based solutions consider each approach individually as a tool, not fully exploiting integration possibilities. Fully integrate corpus-based approaches to improve translation quality and minimize translation effort and cost.
  9. 9. Objectives of the project (3) Topic State-of-the-art and limitations EXPERT solutions Human translator in the loop: Informing users and learning from user feedback In interactive workflows where humans post-edit/complete system translations, translators are not informed about the quality of the translations. The translators’ choice is at best saved for future use. Generate confidence and quality estimation mechanisms to allow these choices to be based on the quality of the TM/MT output. Make use of translators’ feedback as produced at translation time to improve the system on the fly.
  10. 10. Work packages WP1: Management (UoW) WP7: Training (UvA) WP8: Dissemination (Pangeanic) WP2: User perspective (UMA) WP3: Data collection (Translated) WP4: Language technology, domain ontologies and terminologies (USSAR) WP5: Learning from and informing translators (USFD) WP6: Hybrid corpus-based approaches (DCU)
  11. 11. Projects ESR1 Investigation of translators’ requirements from translation technologies UMA WP2 ESR2 Investigation of an ideal translation workflow for hybrid translation approaches USAAR WP2 ESR3 Collection and preparation of multilingual data for multiple corpus-based approaches to translation UMA WP3 ESR4 Use of language technology to improve matching & retrieval in translation memories UoW WP4
  12. 12. Projects (2) ESR5 Use of terminologies and ontologies to improve corpus-based approaches to translation USAAR WP4 ESR6 Learning from human feedback on the quality of the translations USFD WP5 ESR7 Estimating the confidence of corpus-based approaches to translation and the quality of the translated texts USFD WP5 ESR8 Investigation of how each individual corpus-based translation approach (TM, EBMT and SMT) can benefit from each other DCU WP6
  13. 13. Projects (3) ESR9 ESR10 ESR11 ESR12 Investigation of the ideal infrastructure for computer-aided translation: pipeline with NLP tools for pre/post-processing, SMT, EBMT and TM techniques–a hybrid CAT tool Exploiting hierarchical alignments for linguistically-informed SMT models to meet the hybrid approaches that aim at compositional translation Exploiting hierarchical alignments for a semantically-enriched SMT system that offers an extension to existing TMs to allow incremental, recursive partial match of the input using hierarchical constructions containing variables Investigation of methodologies to evaluate the improved SMT, EBMT and TM prototypes and new hybrid computer-aided translation technology proposed in EXPERT DCU WP6 UvA WP6 UvA WP6 UoW WP6
  14. 14. Projects (4) ER1 Investigation of automatic methods preparation of multilingual data ER2 ER3 for collection & Translated WP3 Implementation and evaluation (including user aspects) of the improved SMT, EBMT and TM prototypes proposed in EXPERT Hermes WP6 Implementation and evaluation of the new hybrid computeraided translation technology proposed in EXPERT Pangeanic WP6
  15. 15. Consortium  Academic partners:  University of Wolverhampton, UK – coordinator  Universidad de Malaga, Spain  University of Sheffield, UK  Universitaet des Saarlandes, Germany  Dublin city University, Ireland  Universiteit Van Amsterdam, Netherlands  Private sector:  Pangeanic, Spain  Translated SRL, Italy  Hermes, Spain  Associated partners:  Celer Soluciones S.L., Spain  Wordfast, France