Syntactic complexity of interpreted, L2 and L1 speech: A constrained language perspective

This study explored the use of phraseological frames (p-frames), a type of lexical bundle, by simultaneous interpreters as a strategy for managing cognitive loads. Specifically, using a comparable corpus of United Nations Security Council conferences, the study employed kfNgram to automatically identify the p-frames, and investigated their variations, regarding frequency, fixedness, structures, and functions among L1, L1–L2, and L2–L1 texts, which differ in cognitive loads due to task complexity and directionality of interpreting. The findings indicated that interpreters used more p-frames as cognitive loads increased; No significant difference was identified in fixedness as all texts tended to employ heavily formulaic and relatively fixed p-frames. Directionality correlated with grammatical preferences, with retour interpreting relying more on content-word-based p-frames. Additionally, task complexity correlated with functional preferences, with self-expression characterized by more stance expressions in the simple task. This study innovatively addressed the interaction of two factors that generate cognitive loads in interpreting and filled a research gap by providing empirical evidence on how directionality affects the use of formulaic language.

Section: Connecting Fs With Task and Environment Characteristicsmentioning

confidence: 99%

Chunking in simultaneous interpreting: the impact of task complexity and translation directionality on lexical bundles

Huang,

Li,

Guo

2023

“…This deliberate choice leads to a decrease in lexical variety and sophistication in the interpreted product, helping to mitigate processing difficulties (Lv and Liang, 2019). This finding in terms of lexical simplification responds to the notion that simultaneous interpreters, burdened by heavier cognitive load and constrained by time limitations, may inhibit the generation of complicated and lengthy sentence structures, resulting in a simplification of syntax (Liu et al, 2023).…”

Section: Simplified Vocabulary Features In Interpreted Textsmentioning

confidence: 84%

Lexical density, lexical diversity, and lexical sophistication in simultaneously interpreted texts: a cognitive perspective

Liu,

Dou

2023

Simultaneous interpreting (SI) is a cognitively demanding task that imposes a heavy cognitive load on interpreters. Interpreting into one’s native (A language) or non-native language (B language), known as interpreting directionality, involves different cognitive demands. The cognitive requirements of simultaneous interpreting as well as interpreting directionality affect the interpreting process and product. This current study focused on the lexical features of a specially designed corpus of United Nations Security Council speeches. The corpus included non-interpreted speeches in US English (SubCorpusE), and texts interpreted from Chinese into English (A-into-B interpreted texts, SubCorpusC-E) and from Russian into English (B-into-A interpreted texts, SubCorpusR-E). Ten measures were used to analyze the lexical features of each subcorpus in terms of lexical density, lexical diversity, and lexical sophistication. The three subcorpora were regrouped into two pairs for the two research questions: SubCorpusR-E versus SubCorpusE and SubCorpusR-E versus SubCorpusC-E. The results showed that the interpreted texts in SubCorpusR-E exhibited simpler vocabulary features than the non-interpreted texts in SubCorpusE. In addition, compared with the A-into-B interpreted texts, the B-into-A interpreted texts demonstrated simplified lexical characteristics. The lexical features of the interpreted texts reflect that experienced simultaneous interpreters consciously adopt a simplified vocabulary approach to manage the cognitive load during simultaneous interpreting. This study provides new insights into the cognitive aspects of simultaneous interpreting, the impact of directionality, and the role of lexical strategies. These findings have practical implications for interpreter training, professional growth, and maintaining interpreting quality in diverse settings.

“…Such studies have confirmed that (translation) directionality, a central issue in Translation and Interpreting Studies (cf. Schwieter and Ferreira, 2017 , p. 90–105; Baker and Saldanha, 2020 , p. 152–156; Liu Y. et al, 2023 ), has an effect on cognitive loading, while L2 translation is generally considered more cognitively demanding than L1 translation ( Pavlović, 2007 ; Chang et al, 2008 ; Chang, 2009 ; Chang, 2011 ), and that this could lead to lower translation quality ( Whyatt, 2019 ). However, it is worth noting that “physiological” data to support “translation asymmetry” argued by the ICM are extremely limited.…”

Section: Research Backgroundmentioning

confidence: 99%

Translation directionality and the Inhibitory Control Model: a machine learning approach to an eye-tracking study

Chang

Chen

2023

IntroductionBased on such physiological data as pupillometry collected in an eye-tracking experiment, the study has further confirmed the effect of directionality on cognitive loads during L1 and L2 textual translations by novice translators, a phenomenon called “translation asymmetry” suggested by the Inhibitory Control Model, while revealing that machine learning-based approaches can be usefully applied to the field of Cognitive Translation and Interpreting Studies.MethodsDirectionality was the only factor guiding the eye-tracking experiment where 14 novice translators with the language combination of Chinese and English were recruited to conduct L1 and L2 translations while their pupillometry were recorded. They also filled out a Language and Translation Questionnaire with which categorical data on their demographics were obtained.ResultsA nonparametric related-samples Wilcoxon signed rank test on pupillometry verified the effect of directionality, suggested by the model, during bilateral translations, verifying “translation asymmetry” at a textual level. Further, using the pupillometric data, together with the categorical information, the XGBoost machine-learning algorithm yielded a model that could reliably and effectively predict translation directions.ConclusionThe study has shown that translation asymmetry suggested by the model was valid at a textual level, and that machine learning-based approaches can be gainfully applied to Cognitive Translation and Interpreting Studies.