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Article published In: Babel
Vol. 68:2 (2022) ► pp.236–266

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Navigating learner data in translator and interpreter training

Insights from the Chinese/English Translation and Interpreting Learner Corpus (CETILC)

 | Hong Kong Baptist University
 | Hong Kong Metropolitan University
 | The Hang Seng University of Hong Kong
Published online: 15 April 2022
DOI logo with link
https://doi.org/10.1075/babel.00260.pan
Abstract
The development of technology, in particular, innovations in natural language processing and means to explore big data, has influenced different aspects in the training of translators and interpreters. This paper investigates how learner corpora and their research contribute to the teaching and learning of translation and interpreting. It starts with a review of the evolvement of learner corpora in translator and interpreter training. Drawing on data from the Chinese/English Translation and Interpreting Learner Corpus (CETILC), a learner corpus developed for the study of lexical cohesion, the paper introduces three case studies to illustrate the possibilities of exploring learner data through human annotation, machine-facilitated human annotation, and finally human-supervised/edited machine annotation. The findings of the case studies suggest the complexity of learner language and its intricate relationships with various factors concerning the learner, text, and task. The paper ends with a discussion of the great potentials of purposely made learner corpora such as the CETILC in translator and interpreter training, as well as the application of learner corpora in (semi-) automatic processing of learner texts.
Résumé
Le développement de la technologie, notamment le traitement automatique des langues et les nouveaux moyens d’explorer les mégadonnées, a beaucoup influencé la formation des traducteurs et des interprètes. Cet article a donc pour objectif d’explorer comment les corpus d’apprenants et les recherches sur des corpus d’apprenants peuvent contribuer à l’enseignement et à l’apprentissage de la traduction et de l’interprétation. Nous passons d’abord en revue l’évolution des corpus d’apprenants dans la formation des traducteurs et des interprètes. Ensuite, à partir des données issues du Corpus d’apprenants en traduction et en interprétation chinois-anglais (CETILC), un corpus développé pour l’étude de la cohésion lexicale, nous proposons trois cas d’étude pour illustrer les possibilités d’explorer les données des apprenants à travers l’annotation manuelle, l’annotation manuelle assistée par ordinateur et l’annotation automatique supervisée et éditée par l’humain. Les résultats de ces cas d’étude montrent la complexité de la langue des apprenants, ainsi que les relations complexes entre la langue des apprenants et divers facteurs tels que l’apprenant lui-même, le texte et la tâche à accomplir. Finalement, nous observons la grande utilité des corpus d’apprenants, comme le CETILC, qui sont spécifiquement conçus pour la formation des traducteurs et des interprètes. Nous abordons aussi l’application des corpus d’apprenants au traitement (semi-)automatique des textes produits par les apprenants.
Article outline
  • 1.Introduction
  • 2.Learner corpora in translation and interpreting
  • 3.Learner corpora for translator and interpreter training: Perspectives from textual level performance
    • 3.1The Chinese/English Translation and Interpreting Learner Corpus (CETILC)
    • 3.2Case study 1: Learner differences in textual level translation performance
      • 3.2.1Data description
      • 3.2.2Findings and discussion
    • 3.3Case study 2: Learner differences in the employment of cohesive devices
      • 3.3.1Data description
      • 3.3.2Findings and discussion
    • 3.4Case study 3: Learners’ employment of lexical cohesive devices under different contexts
      • 3.4.1Data description
      • 3.4.2Annotation of lexical cohesive devices
      • 3.4.3Findings and discussion
  • 4.Implications and future directions
    • 4.1T&I learner corpora: Implications from the three case studies
    • 4.2Prospects for (semi-)automatic processing of T&I learner corpora
    • 4.3Future development and application of the CETILC
  • 5.Conclusion
  • Acknowledgements
  • References
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