Chinese-English Translation Of Soft News A Functional Approach

The emerging popularity of soft news and the strengthened intercultural communication call for more translations in this field. Thus, soft news translation opens a new field for translation studies and poses controversial and challenging questions for translators and translation scholars.As the soft news translation differs from the equivalence-based translation practice in that it centers on the target audience, it is not just a Source Language (SL)-Target Language (TL) linguistic operation, but often a specialized adaptation process depending on factors of the target culture. With the functionalist theory as the rationale, this thesis attempts to bridge the gap between the translation practice of soft news from Chinese to English and the long established equivalence-based translation criteria. Furthermore, the author attempts to offer a more operational top-down model in terms of the functional translation approach.The text-type analysis of soft news and the literature review of the functionalist theory make us recognize that how specific this field of translation is and how appropriate the functional approach is to be applied. After analyzing key steps in the translation process, the author applies the functionalist theory to the corpus and generalizes the strategies adopted in soft news translation. The DTS method adopted for the analysis features a two-stage procedure: discovery and justification. The preliminary findings of the first stage are tested in the second stage where the whole corpus is analyzed. And then the author moves on to the findings and implications from the analysis. The classification of strategies and the tentative proposal of the top-bottom model in terms of functional translation approach may offer a guideline for translators in their practice. And the implications in the field of translation pedagogy and quality assessment may shed light on further study and application. Lastly, the limitations of this thesis may reveal some perspectives for future research and improvement.