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(FP7-PEOPLE-2010-ITN-263954)

OPTIMALE Symposium Rennes, 6 June 2013. The handling of translation metadata in TM/MT environments: Insights from process research. (FP7-PEOPLE-2010-ITN-263954). Carlos da Silva Cardoso Teixeira Universitat Rovira i Virgili (Tarragona, Spain). Defining metadata.

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(FP7-PEOPLE-2010-ITN-263954)

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  1. OPTIMALE Symposium Rennes, 6 June 2013 The handling of translation metadata in TM/MT environments: Insights from process research (FP7-PEOPLE-2010-ITN-263954) Carlos da Silva Cardoso TeixeiraUniversitat Rovira i Virgili (Tarragona, Spain)

  2. Defining metadata

  3. Defining metadata (cont’d)

  4. Motivation • Integration ofTM and MT  different workflows • Translation vs. Post-editing vs. Mixed (interactive) (pretranslated) (?) Metadata  Actual impact ? .

  5. Research questions Speed: Will you translate faster? Quality: Will you translate better? Effort: Will you feel more tired? • Explanation:Do metadataplay a role? How do the differences between the environments affect translators? Satisfaction ?

  6. How to test 10 expert translators(English > Spanish) 2 comparable source texts (~520 words, 28 segments) 4 types of translation proposals 7 x Exact matches 7 x High Fuzzy (85-99%) 7 x Low Fuzzy (70-84%) 7 x Machine translation 2 environments • a visual or interactive environment • a blind or pre-translation environment

  7. Visual vs. Blind in TM/2

  8. Data collection methods Keystroke logging Screen recording Eye tracking Retrospective interviews Quality review

  9. Results: Speed • Types of environment: V: Visual environment (metadata available) B: Blind environment (metadata not available) • Types of proposals: • E: Exact matches • H: High-range fuzzy matches (85%-99%) • L: Low-range fuzzy matches (70%-84%) • M: Machine Translation

  10. Results: Effort (typing) • Types of environment: V: Visual environment (metadata available) B: Blind environment (metadata not available) • Types of proposals: • E: Exact matches • H: High-range fuzzy matches (85%-99%) • L: Low-range fuzzy matches (70%-84%) • M: Machine Translation

  11. Results: Quality • Types of environment: V: Visual environment (metadata available) B: Blind environment (metadata not available) • Types of proposals: • E: Exact matches • H: High-range fuzzy matches (85%-99%) • L: Low-range fuzzy matches (70%-84%) • M: Machine Translation

  12. Results: Comparison SPEED-1 EFFORT (TYPING) QUALITY-1 • Types of environment: V: Visual environment (metadata available) B: Blind environment (metadata not available) • Types of proposals: • E: Exact matches in TM/2 • H: High-range fuzzy matches (85%-99%) in TM/2 • L: Low-range fuzzy matches (70%-84%) in TM/2 • M: Machine-Translation proposals in TM/2

  13. Interviews (1)

  14. Interviews (2)

  15. Reality vs. Perception Conjectures Higher cognitive effort when no metadata (≠ typing effort) Different cognitive strategies for processing MT and TM proposals (E, H, L) Satisfaction ~ familiarity, access to information, possibility to choose

  16. Implications/applications • Translators: job satisfaction, or at least task satisfaction • Vendorsand clients: productivity, payment schemes • Tool manufacturers: increase usability of tools  sales • MT researchers: quality estimation for MT proposals • Translator training: teach new required skills • Intellectual significance: how we process information?

  17. Thank you! Merci ! Obrigado! Carlos S. C. Teixeira Intercultural Studies Group Universitat Rovira i Virgili (Tarragona, Spain)

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