literal translation and post-editing processes · post-editing processes michael carl, moritz...
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Literal Translation and Post-editing Processes
Michael Carl, Moritz Schaeffer, Arnt Lykke Jakobsen,
Copenhagen Business School
Germersheim, January, 2015
Literal Translation
• Toury’s (1995) law of interference: “in translation, phenomena pertaining to the make-up of the source text tend to be transferred to the target text”
• Tirkkonen-Condit (2005) monitor model: “It looks as if literal translation is a default rendering procedure, which goes on until it is interrupted by a monitor that alerts about a problem in the outcome.”
Germersheim, January, 2015
Overview
– Mental representation of translation • shared combinatorial nodes (Hartsuiker, 2004)
• experimental method: priming
– A quantitative definition of literal translation • word order and word translation perplexity
– Word translation perplexity • In translation and post-editing
– Translation cycles (inspired by Jakobsen, 2013) • ST reading
• TT production and monitoring
• Verification of produced translation
• Translation revision
Germersheim, January, 2015
Hartsuiker et al (2004)
Germersheim, January, 2015
Priming effects
• Non-conscious form of human memory: spreading activation decreased effort
• Psychological method developed early 1970s
– "yellow" faster recognize "banana“
– DOCTOR faster recognize NURSE than BREAD
• Many types of priming:
– Positive (facilitating, speed up) – negative (slow down)
– Perceptual / Semantic / Conceptual (table – chair)
• Cross modality: image / audio / written / spoken
• Cross languages: syntactic / lexical
Germersheim, January, 2015
Literality in translation
Priming effects are likely to produce literal translations, because literal translations encode linguistic information in a more similar way.
Conscious cognitive effort is required to arrive at more idiomatic translations.
Germersheim, January, 2015
Quantifyable translation literality metric
1. Word order is identical in the source text (ST) and target text (TT)
2. ST and TT items correspond one-to-one
3. Each ST word has only one possible translated form in a given context
Germersheim, January, 2015
Ideal literal translation
1. One-to-one translation correspondences
2. ST-TT identical word order
3. One possible translation per ST word
Germersheim, January, 2015
Less literal translation
1. m-to-n translation correspondences
2. distorted word order
3. several different translations possible per word
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32 translations into Spanish of ”He was given four life sentences”
Germersheim, January, 2015
Total Reading Time (TRT) per character on ST words (GazeS) and Total Reading Time on TT words (GazeT) correlates with translation perplexity
Germersheim, January, 2015
Translation Variation in translation (TT) vs. post-editing (PE)
In a gesture sure to rattle the Chinese Government , Steven Spielberg pulled out of the Beijing Olympics... 8 translators -> 7 versions 7 post-editors -> 3 versions Post-editors: • produce un-idiomatic versions, • 4 out of 7 solutions identical to
MT output are primed by the MT output
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Word translation perplexity in post-
editing (PE) and translation (TRA)
En De and
En Es
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Literal translation and facilitation effects in the translation cycle
Step1: Initial reading of source-text (Gaze on ST)
Step2: Monitoring translation production (Gaze on TT)
Step3: Verification of produced translation (Gaze on ST)
Step4: Translation revision (Gaze on TT)
Behavioral measure: Total reading time (TRT) per character
Germersheim, January, 2015
Step 1: Reading the source-text
• Combinatorial nodes are activated and automatic pre-translation is initiated, pre-selecting possible translations and syntactic re-ordering
• TRT per character on ST words correlates with the ST alignment crossing distance (CrossS)
Germersheim, January, 2015
TRT per character on ST words (GazeS) correlates with the ST alignment crossing distance (CrossS)
Step 1
Germersheim, January, 2015
Step 2: Typing and monitoring translation
• Combinatorial nodes are activated from TT
• Mind maps the produced TT words back onto equivalent ST items, controlling whether the emerging TT is equal to the ST chunk.
• TRT per character on TT words correlates with the TT alignment crossing distance (CrossT)
Germersheim, January, 2015
TRT per character on TT words (GazeT) correlates with the TT alignment crossing distance (CrossT)
Step 2
Germersheim, January, 2015
Step 3: Verification of produced translation
• Switch visual attention again back to the ST, thereby gazing at the ST segment which corresponds to the current TT words
• TRT per character on ST words correlates with the TT alignment crossing distance (CrossT)
Germersheim, January, 2015
TRT per character on ST words (GazeS) correlates with the TT alignment crossing distance (CrossT)
Step 3
Germersheim, January, 2015
Step 4: Translation revision
• Reading the TT in a TT revision mode
• TRT per character on TT words correlates with the ST alignment crossing distance (CrossS)
Germersheim, January, 2015
TRT per character on TT words (GazeT) correlates with the ST alignment crossing distance (CrossS)
Step 4
Germersheim, January, 2015
Correlation of reading times and local ST – TT distortion (Cross)
Trt Source Trt Target
CrossS Step1: Initial reading of a source-text chunk
Step4: Translation revision
CrossT Step3: Verification of produced translation
Step2: Typing and monitoring translation production
Germersheim, January, 2015
Conclusions
• Model of shared combinatorial nodes explains literal translation through spreading activity
• Measure to quantify literality in translation
• Correlation between literality and total reading time
• More literal translations are less effortful for
– ST reading for translation and pre-translation
– Translation production and monitoring
– Translation revision