The effect of informational load on disfluencies in interpreting

Abstract

This article attempts to measure the cognitive or informational load in interpreting by modelling the occurrence rate of the speech disfluency uh(m). In a corpus of 107 interpreted and 240 non-interpreted texts, informational load is operationalized in terms of four measures: delivery rate, lexical density, percentage of numerals, and average sentence length. The occurrence rate of the indicated speech disfluency was modelled using a rate model. Interpreted texts are analyzed based on the interpreter's output and compared with the input of non-interpreted texts, and measure the effect of source text features. The results demonstrate that interpreters produce significantly more uh(m)s than non-interpreters and that this difference is mainly due to the effect of lexical density on the output side. The main source predictor of uh(m)s in the target text was shown to be the delivery rate of the source text. On a more general level of significance, the second analysis also revealed an increasing effect of the numerals in the source texts and a decreasing effect of the numerals in the target texts.

The cognitive load of interpreters in the European Parliament

“…However, delivery rate was significant only for the source texts, whereas lexical density was significant only in the comparison between the interpreted (target) texts and the monolingual texts. The results reported by Plevoets and Defrancq (2016) thus clearly reflect cognitive load, but they also underline the need to differentiate between the cognitive demands of comprehension or listening and those of language production (which we will call 'input load' and 'output load' , respectively). Corpus-based research is still sporadic in interpreting studies, despite Shlesinger's (1998) plea for its more widespread adoption.…”

“…A crucial aspect of our analysis is that we will study the four informational load indicators identified above (delivery rate, lexical density, proportion of num-bers and formulaicity) in both the source text and its simultaneous interpretation (following Plevoets & Defrancq 2016). The reason is that the interpreter's cognitive load is expected to increase not only when informational load is higher in the source text, but also when the interpretation is faster and/or lexically/numerically denser.…”

“…The study described by Plevoets and Defrancq (2016) is not without its limitations. Firstly, the unit of analysis was each text as a whole: this was acknowledged in the study, the authors' suggestion being a sentence-level analysis.…”

“…By contrast, Plevoets and Defrancq (2016) analysed the frequency of the disfluency uh(m) in a corpus of interpretations at the European Parliament, the EPICG (Defrancq 2015). Alongside a 'classic' comparison of source speeches with target speeches, the method was also 'comparable' (Baker 1993) in that Dutch interpretations recorded live at the European Parliament were compared to non-interpreted Dutch speeches by members of the Dutch and Belgian parliaments.…”

“…Alongside a 'classic' comparison of source speeches with target speeches, the method was also 'comparable' (Baker 1993) in that Dutch interpretations recorded live at the European Parliament were compared to non-interpreted Dutch speeches by members of the Dutch and Belgian parliaments. Based on these corpus data, Plevoets and Defrancq (2016) examined the role of delivery rate, lexical density, frequency of numbers and average sentence length as independent variables affecting the occurrence rate of uh(m): delivery rate and lexical density proved to be important predictors. However, delivery rate was significant only for the source texts, whereas lexical density was significant only in the comparison between the interpreted (target) texts and the monolingual texts.…”