literal translation and post-editing processes · post-editing processes michael carl, moritz...

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

Germersheim, January, 2015

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

Germersheim, January, 2015

Word translation perplexity in post-

editing (PE) and translation (TRA)

En De and

En Es

Germersheim, January, 2015

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