Navigating learner data in translator and interpreter training

Jun Pan, B. Wong, Honghua Wang
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引用次数: 4

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.
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翻译培训中的学习者数据导航
技术的发展,特别是自然语言处理和探索大数据手段的创新,影响了笔译和口译培训的各个方面。本文探讨了学习者语料库及其研究对翻译教学的贡献。本文首先回顾了学习者语料库在翻译培训中的发展。本文利用汉英翻译学习者语料库(CETILC)的数据,介绍了三个案例研究,以说明通过人工注释、机器辅助人工注释和人工监督/编辑机器注释来探索学习者数据的可能性。案例研究的结果表明,学习者语言的复杂性及其与学习者、文本和任务等各种因素之间的复杂关系。本文最后讨论了有意制作的学习者语料库(如cettic)在翻译培训中的巨大潜力,以及学习者语料库在学习者文本(半)自动化处理中的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
1.00
自引率
0.00%
发文量
48
审稿时长
7 weeks
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