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Joke Daems joke.daems@ugent.be lt3.ugent.be/en/projects/robot Supervised by:

Two sides of the same coin assessing translation quality through adequacy and acceptability error analysis. Joke Daems joke.daems@ugent.be www.lt3.ugent.be/en/projects/robot Supervised by: Lieve Macken, Sonia Vandepitte, Robert Hartsuiker. What makes error analysis so complicated?.

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Joke Daems joke.daems@ugent.be lt3.ugent.be/en/projects/robot Supervised by:

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  1. Two sides of the same coinassessing translation quality through adequacy and acceptability error analysis Joke Daems joke.daems@ugent.be www.lt3.ugent.be/en/projects/robot Supervised by: Lieve Macken, Sonia Vandepitte, Robert Hartsuiker

  2. What makes error analysis so complicated? “There are some errors for all types of distinctions, but the most problematic distinctions were for adequacy/fluencyand seriousness.” – Stymne & Ahrenberg, 2012 • Does a problem concern adequacy, fluency, both, neither? • How do we determine the seriousness of an error?

  3. Two types of quality “Whereas adherence to source norms determines a translation'sadequacyas compared to the source text, subscription to norms originating in the target culture determines its acceptability.” - Toury, 1995  Why mix?

  4. 2-step TQA approach

  5. Subcategories

  6. Acceptability: fine-grained

  7. Adequacy: fine-grained

  8. How serious is an error? “Different thresholds exist for major, minor and critical errors. These should be flexible, depending on the content type, end-user profile and perishability of the content.” - TAUS, error typology guidelines, 2013  Give different weights to error categories depending on text type & translation brief

  9. Reducing subjectivity • Flexible error weights • More than one annotator • Consolidation phase

  10. TQA: Annotation (brat) 1) Acceptability 2) Adequacy

  11. Application example: comparative analysis

  12. Next step:diagnostic & comparative evaluation • What makes a ST-passage problematic? • How problematic is this passage really? (i.e.: how many translators make errors) • Which PE errors are caused by MT? • Which MT errors are hardest to solve?  Link all errors to corresponding ST-passage

  13. Source text-related error sets • ST: Changes in the environment that are sweeping the planet... • MT: Veranderingen in de omgeving die het vegen van de planeet tot stand brengen... (wrong word sense) "Changes in the environment that bring about the brushing of the planet..." • PE1: Veranderingen in de omgeving die het evenwicht op de planeet verstoren... (other type of meaning shift) "Changes in the environment that disturb the balance on the planet..." • PE2: Veranderingen in de omgeving die over de planeet rasen... (wrong collocation + spelling mistake) "Changes in the environment that raige over the planet..."

  14. Application example: impact of MT errors on PE

  15. Summary • Improve error analysis by: • judging acceptability and adequacy separately • making error weights depend on translation brief • having more than one annotator • introducing consolidation phase • Improve diagnostic and comparative evaluation by: • linking errors to ST-passages • taking number of translators into account

  16. Open questions • How can we reduce annotation time? • Ways of automating (part) of the process? • Limit annotation to subset of errors? • How to better implement ST-related error sets? • Ways of automatically aligning ST, MT, and various TT’s at word-level?

  17. Thank you for listening For more information, contact: joke.daems@ugent.be Suggestions? Questions?

  18. Quantification of ST-related error sets

  19. Inter-annotator agreement

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