Assessing effects of source text complexity on L2 learners’ interpreting performance: a dependency-based approach

Abstract Based on data from the English-Chinese interpretation corpus, we examined the relationship of source text complexity, captured using newly-developed dependency-based and traditional indices, to L2 learners’ interpreting performance captured using complexity, accuracy, and fluency. Best subsets regression and Poisson regression models yielded that the effectiveness of dependency-based indices including mean dependency distance, maximum dependency distance, dependency direction, and root distance, has been demonstrated across various dimensions of L2 learners’ performance. In contrast to the mixed results obtained from traditional indices, the consistent effect of dependency-based indices in these dimensions sheds light on the workings of cognitive processing. These findings provide preliminary support for the impact of dependency-based indices of source text on L2 learners’ interpreting performance, aiding in operationalizing task difficulty in L2 interpreting pedagogy. Moreover, they constitute product-based evidence for understanding bilingual switching.