The Correlation Between Translator Experience And Translation Speed

It is generally believed that the ability to finish a specific task in a quick and efficientmanner is a natural result of extensive experience. According to some empirical translationstudies, experienced translators work faster than novice translators. But other researchesobserve that experienced translators do not necessarily perform faster and a―translation-does-not-get-easier‖phenomenon seems to exist. In sum, the results of earlystudies on the correlation between translator experience and translation speed show ahighly incoherent picture, which call for a further exploration.Translation speed is an external indicator of translators‘mental mechanism and thelevel of cognitive load. Viewed from a cognitive information processing perspective, thetranslation process, as a complex mental activity highly dependent on the use of language,is mainly composed of controlled processing which is slower and requires a relatively largeamount of cognitive capacity. But it should be noted that a large amount of practice, tosome extent, is conducive to enhancing the efficiency of cognitive resources allocation.With an adequate amount of practice, some processing might becomes automatic, which isless cognitively demanding and much faster. Thus it can be inferred that translation speedis related to translators‘degree of automaticity, but so far few researches have beendevoted to this topic.Based on the automaticity mechanism theories derived from cognitive science as wellas the translation processing types proposed respectively by Anette de Groot, SusanneG pferich and Friederike Prassl, this dissertation argues that there exist two types oftranslation processing, i.e. instance-based processing which involves direct retrieval ofrepresentation from long-term memory and direct pattern matching, and algorithm-basedprocessing which involves taking algorithm steps according to the specific context,including restructuring, reasoning, transfer and evaluation. There are mainly twocompeting mechanisms to explain the shift from controlled processing to automaticprocessing: algorithm-strengthening theories and instance-based theories. The formerargues that the enhancement of automaticity is attributed to faster algorithm-based processing, while the latter advocates the shift from algorithm-based processing toinstance-based processing.Judged from the consistency and stability of content, translation tasks can be dividedinto two types: well-structured translation tasks and ill-structured translation tasks. Eachexperience with well-structured translation tasks leaves a memory trace or instancerepresentation that can be retrieved when the task repeat itself. The number of instancesstored in long-term memory grows with the number of practice trials, building up atask-relevant knowledge base, and some algorithm-based processing will shift toinstance-based processing. In this way, translators can benefit from extensive practice, andtheir translation processing become less cognitively demanding and more efficient. Inill-structured tasks which contains diversified and fragmented data and information,translators constantly need to assemble new and unlearned sequences of behavior andplanning. Therefore it is hard to build up a reliable knowledge base and make a shift toinstance-based processing. Also, algorithm-based processing profit only little from practice.As a result, experienced translators might be still subject to a large cognitive load and notnecessarily work faster when handling ill-structured tasks.Taking the above arguments into consideration, this dissertation proposes twohypotheses:Hypothesis1: Translation task types have a significant moderating effect on thecorrelation between translator experience and translation speed.Hypothesis2: Given that Hypothesis1is supported, the enhancement of translationspeed of experienced translator when handling well-structured tasks is mainly attributed toa shift from algorithm-based processing to instance-based processing.An empirical process-oriented study is conducted to test these hypotheses.11subjectswith different level of experience are invited to fulfill two types of translation tasks.Multiple research tools including retrospective think-aloud protocol, key-stroke logging,screen recording and questionnaire are used to observe the subjects‘translation process.R-project and Inpulog software are used to analyze the collected data, and subjects‘retrospective think-aloud protocol are also analyzed. The results of hypothesis test are as below:Hypothesis1: Supported. Translation task types have a significant moderating effecton the correlation between translator experience and translation speed. In well-structuredtranslation tasks, there is significant positive correlation between translator experience andtranslation speed. In ill-structured translation tasks, there is no significant correlationbetween translator experience and translation speed.Hypothesis2: Supported. In well-structured translation tasks, the proportion ofinstance-based processing has a significant negative correlation with total translation time.