Identifying fluency parameters for a machine-learning-based automated interpreting assessment system.
Fluency is an important yet difficult-to-measure criterion in interpreting assessment. This empirical study of English-Chinese consecutive interpreting aims to identify fluency parameters for a machine-learning-based automated assessment system. The main findings include: (a) empirical evidence supp...
| Publicado en: | Perspectives: Studies in Translatology Vol. 32; no. 2; pp. 278 - 295 |
|---|---|
| Autores principales: | , |
| Formato: | Artículo |
| Publicado: |
Taylor & Francis Ltd
Apr2024
|
| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=176476801&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 176476801 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 0907676X S2F jtl: Perspectives: Studies in Translatology issn: 0907676X maglogo: N pubinfo: dt: Apr2024 vid: 32 iid: 2 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 176476801 10.1080/0907676X.2022.2133618 ppf: 278 ppct: 17 formats: fmt: – @attributes: type: T – @attributes: type: P size: 2.2MB tig: atl: Identifying fluency parameters for a machine-learning-based automated interpreting assessment system. aug: au: Wang, Xiaoman Wang, Binhua affil: School of Language, Culture and Society, University of Leeds, Leeds, UK su: Median (Mathematics) Machine learning Scoring rubrics Dependent variables Empirical research sug: subj: Median (Mathematics) Machine learning Scoring rubrics Dependent variables Empirical research keyword: automated assessment Consecutive interpreting descriptive statistical analysis fluency parameters ab: Fluency is an important yet difficult-to-measure criterion in interpreting assessment. This empirical study of English-Chinese consecutive interpreting aims to identify fluency parameters for a machine-learning-based automated assessment system. The main findings include: (a) empirical evidence supports the choice of the median values as the cut-offs for unfilled pauses and articulation rate; (b) it informs the selection of outliers as particularly long unfilled pauses, relatively long unfilled pauses, particularly slow articulation and relatively slow articulation; (c) number of filled pauses, number of unfilled pauses, number of relatively slow articulation, mean length of unfilled pauses, mean length of filled pauses can be chosen to build machine-learning models to predict interpreting fluency in future studies as they can explain the variance of established temporal measures and show stronger explanatory power than dependent variables when predicting scores. The study identifies assessment rubrics on an empirical basis and provides a methodological solution to automate the labour-intensive tasks in interpreting assessments. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Copyright of Perspectives: Studies in Translatology is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. item: Perspectives: Studies in Translatology holder: Taylor & Francis Ltd dt: @attributes: year: 2024 holdings: @attributes: islocal: N |
|---|