尚小奇, 李德超. 数据驱动下的口译分项评估权重研究:母语为汉语的评分员视角[J]. 外国语, 2022, 45(3): 82-92.
引用本文: 尚小奇, 李德超. 数据驱动下的口译分项评估权重研究:母语为汉语的评分员视角[J]. 外国语, 2022, 45(3): 82-92.
SHANG Xiaoqi, LI Dechao. A Data-driven Approach to Exploring Weighting Schemes for Assessing Bi-directional Interpreting Performance: Evidence from Native Chinese-speaking Raters[J]. Journal of Foreign Languages, 2022, 45(3): 82-92.
Citation: SHANG Xiaoqi, LI Dechao. A Data-driven Approach to Exploring Weighting Schemes for Assessing Bi-directional Interpreting Performance: Evidence from Native Chinese-speaking Raters[J].Journal of Foreign Languages, 2022, 45(3): 82-92.

数据驱动下的口译分项评估权重研究:母语为汉语的评分员视角

A Data-driven Approach to Exploring Weighting Schemes for Assessing Bi-directional Interpreting Performance: Evidence from Native Chinese-speaking Raters

  • 摘要:口译分项评估是目前应用较为广泛的口译质量评估方式。然而,关于评估参数权重的设定,口译研究者之间并未达成共识。现有研究大多通过理论推演或问卷调查等方法来探讨权重的分配,而基于评分员真实评分、以数据为驱动的实证研究仍然比较匮乏。在探讨口译权重设定时,现有研究也鲜将方向性考虑在内。鉴于此,本研究以英汉双向口译为研究对象,通过分析八位评分员(母语:汉语; 外语:英语)对50个口译学员(母语:汉语; 外语:英语)的口译录音的评分数据,以探讨如何在各个参数间分配权重。数据分析结果显示:(1)无论何种语言方向,信息始终是最为重要的评估参数( β 1=.351(汉英); β 1=.593(英汉)); (2)无论何种语言方向,表述的权重均位列第二( β 3=.345(汉英); β 3=.381(英汉)); (3)语言在汉英口译评估时的权重排序第三( β 2=.325),而对于英汉口译而言,其权重无法通过统计模型估算出来。本研究的发现可以为英汉双向口译评估标准的设定和口译培训提供重要实证数据。

    Abstract:Analytic rating scales are widely used for assessing interpreting.However, weighting schemes for assessing interpreting reported in previous studies have been largely conceptual and generally pre-determined.Research that investigates weighting based on empirical interpreting assessment data remains scant.And few studies to date have attempted to differentiate between language directions when it comes to weighting.To fill this gap, this study adopts a data-driven approach to exploring weighting schemes for assessing Chinese to English bi-directional interpreting performance.A total of eight raters were invited to evaluate 50 Chinese to English (C-E) interpretations and 50 English to Chinese (E-C) interpretations by trainee interpreters, using an analytic rating scale and a holistic rating scale.Data analysis suggested that: (1) fidelity was the predominant criterion in predicting the candidate's interpreting performance, regardless of interpreting direction ( β 1=.351 for C-E interpreting; β 1=.593 for E-C interpreting); (2) delivery came second among the three assessment criteria, regardless of interpreting direction ( β 3=.345 for C-E interpreting; β 3=.381 for E-C interpreting), and (3) language contributed up to 32.5 percent ( β 2=.325) of the variance in the candidate's interpreting performance in the C-E direction, whereas its predictive power on interpreting performance failed to be detected in the E-C interpreting direction due to statistical concerns.Implications of the findings for interpreter training and for the development and validation of assessment tools for interpreting performance are discussed at the end.

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