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The impact of user interfaces for CAT on time, quality and user satisfaction
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The impact of user interfaces for CAT on time, quality and user satisfaction

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The impact of user interfaces for CAT on time, quality and user satisfaction The impact of user interfaces for CAT on time, quality and user satisfaction Presentation Transcript

  • The impact of user interfaces for CATon time, quality and user satisfaction Author: Lisa Koeman Supervisor: Christof Monz
  • Why?
  • An example: Internet population over the years 1990 0,1% 8% 15% 0,2% 77% Africa Asia Europe Middle East North America Latin America / Caribbean Oceania / Australia
  • An example: Internet population over the years 2011 1990 1% 0,1% 6% 10% 8% 15% 13% 0,2% 3% 44% 77% 23% Africa Asia Europe Middle East North America Latin America / Caribbean Oceania / Australia
  • An example: Internet population over the years 2011 1% 6% 10% 13% 1990 3% 44% 23% Africa Asia Europe Middle East North America Latin America / Caribbean Oceania / Australia
  • How can we make all this information accessible to all people?
  • Machine translationSource text Machine translation
  • But...
  • Machine translation “The machine “De automatischetranslations simply vertalingen eenvoudig arent perfect” zijn niet perfect”
  • However, machine translationdoes bring speed & consistency So...
  • Computer-aided TranslationSource text Machine translation Final translation
  • Whether and to what extent do different translation interfaces impact the post- editing task?
  • How?
  • Functionality analysisEight experimental translation interfaces
  • Experimental design - time per word - correct for translators’ time speed qualityuser satisfaction ￿ ￿ ti (x,y)−tx,y n ¯ ￿ ¯ tx,y ×100 ¯ d= i=1 s
  • Experimental design time qualityuser satisfaction team of judges ￿ κw = 1 − ￿ wij poij wij pcij
  • Experimental design time qualityuser satisfaction survey
  • What?
  • Results: time 9,00 6,75Frequency 4,50 2,25 0 -0,225 -0,150 -0,075 0 0,075 0,150 0,225 0,300 Time per word (in minutes) Source text only Source text + machine translation Machine translation only Source text + machine translation + alternatives
  • Results: quality 1 2 3 4 5 1 2 3 4 51 15 4 0 1 0 1 13 5 2 1 12 9 14 7 4 7 2 4 11 6 6 83 4 7 9 5 14 3 6 3 10 11 84 5 9 11 14 12 4 3 5 12 15 205 10 3 10 8 10 5 2 6 10 12 12 Adequacy Fluency
  • Results: user satisfaction 13% 29% 58% Preference for sentence view Preference for document view No preference
  • One ofthe results
  • Results: an example ‘Document view’ ‘Sentence view’Signi cantly less time Signi cantly better quality Signi cantly preferred
  • Inconclusion
  • 1Presented a way to compare translation interfaces
  • 2 Found signi cant differences intime, quality and user satisfaction between translation interfaces
  • 3 Contributed to existing CAT researchand looked at translator behaviour
  • 4 Results can be used as recommendations for the development of translation interfaces,although further research is recommended
  • Any questions?contact: lisa.koeman@student.uva.nl