Differentiating Interpreting Types: Connecting Complex Networks to Cognitive Complexity

Abstract

Prominent interpreting models have illustrated different processing mechanisms of simultaneous interpreting and consecutive interpreting. Although great efforts have been made, a macroscopic examination into interpreting outputs is sparse. Since complex network is a powerful and feasible tool to capture the holistic features of language, the present study adopts this novel approach to investigate different properties of syntactic dependency networks based on simultaneous interpreting and consecutive interpreting outputs. Our results show that consecutive interpreting networks demonstrate higher degrees, higher clustering coefficients, and a more important role of function words among the central vertices than simultaneous interpreting networks. These findings suggest a better connectivity, better transitivity, and a lower degree of vocabulary richness in consecutive interpreting outputs. Our research provides an integrative framework for the understanding of underlying mechanisms in diverse interpreting types.