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

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
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.
Keywords: learner corpus, translator training, interpreter training, lexical cohesion, corpus linguistics
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
Published online: 15 April 2022
DOI logo
https://doi.org/10.1075/babel.00260.pan
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