Translating Metadiscourse Features in TED Political Talks: A Parallel Corpus-Based Investigation of Semantic Underspecification in English and Persian

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Article Type:
Research/Original Article (دارای رتبه معتبر)
Abstract:

Metadiscourse features are considered important elements of discourse that are used to establish communication between different parts of discourse as well as to help audience (readers and listeners) in understanding the functional relationships. The aim of this study was to investigate these metadiscourse features and to conduct a comparative corpus-based study on some of the most important metadiscourse features in English and Persian. To this end, ten Ted talks, focusing on the political issues in English, were randomly selected and, along with their Persian translations, were added to Sketch Engine corpus software. Hyland’s (2005) framework was selected to analyze and categorize metadiscourse features. In this classification, metadiscourse features are divided into two main categories, namely, interactive and interactional. Transitions belong to the interactive category. The comparative analysis between these elements and their Persian translations suggested that the translation process of these elements can be described in form of two general strategies; literal translation and underspecification. Underspecification process consists of three types, namely, deletion, extension and restriction. Other findings of this study highlighted the significant relationship between the type of underspecification and the type of strategies adopted. Using statistical and quantitative analysis, it was also found that strong transitions are often translated literally, while weak transitions such as “and” are sometimes translated literally and sometimes with underspecification. The findings of this study can be used in future research in the field of corpus-based translation studies and contrastive linguistics.

Language:
Persian
Published:
Language and Translation Studies, Volume:53 Issue: 2, 2020
Pages:
127 to 154
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