THE PRACTICE OF USING PARALLEL CORPORA IN SIMULTANEOUS TRANSLATION
PDF

Keywords

parallel, corpora, translation, simultaneous, text, linguistic, resource, analysis, word, strategies.

Abstract

The practice of using parallel corpora in simultaneous translation represents a modern, data-driven approach aimed at improving translation quality and interpreter performance. Parallel corpora — collections of bilingual or multilingual texts aligned at the sentence or discourse level — provide an opportunity to analyze authentic linguistic correspondences within the translation process. Through such corpora, interpreters can study lexical equivalence, syntactic transformations, idiomatic expressions, and pragmatic adaptations. In the field of simultaneous translation, parallel corpora help interpreters expand their vocabulary, develop context-based translation strategies, and strengthen the cognitive flexibility required for real-time interpreting. Moreover, they serve as an essential source for creating specialized glossaries and educational materials in domains such as law, medicine, and diplomacy. Integrating corpus-based methods into interpreter training programs supports empirical analysis of translation phenomena and promotes evidence-based teaching in translation studies. Thus, the use of parallel corpora contributes to achieving linguistic accuracy, consistency, and a stronger link between translation theory and practice.

PDF

References

Alhassan Sabtan & Omar (2021), Using Parallel Corpora in the Translation Classroom, Arab World English Journal (AWEJ) Volume 12. Number 1. (pp. 41)

Monti, C., Bendazzoli, C., Sandrelli, A., & Russo, M. (2005).

Studying Directionality in Simultaneous Interpreting through an Electronic Corpus: EPIC (European Parliament Interpreting Corpus).

Meta: Translators’ Journal, 50(4), 1047–1065 https://doi.org/10.7202/019850ar

A corpus-based study of metadiscourse features in Chinese-English simultaneous interpreting(09November,2023) https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2023.1269669/full