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Tourism information media is aimed at giving positive image of Indonesian tourism to international tourists. In linguistics, especially in systemic functional linguistics, herewith SFL, the attempt to create positive or negative image of a text is studied through appraisal theory. It deals with how the authors position themselves and how the readers are positioned in the text. However, when dealing with bilingual texts, the meaning shift is highly likely because the translation of the source language may not have the same appraisal features, i.e. attitude, engagement, and graduation. Therefore, an SFL-based translation model is needed to accommodate this issue. As far as the authors observe, there is no SFL-based translation model accommodating tourism texts (Nugroho, Septemuryantoro, and Lewa, 2017). The purpose of this paper is to develop an SFL-based translation model by intertwining appraisal theory (Martin and Rose, 2013), translation techniques (Molina and Albir, 2002), and translation quality assessment (Nababan et al, 2012). By applying critical literature review, the authors analyzed the application of these theories in sample data taken from four information media, namely brochure, booklet, website, and book. Furthermore, by using Flesch readability test, the authors were able to select some parts of the media which are considered “hard to read”. The result of this study reveals that there is a minimum shift of appraisal features found in the bilingual tourism media. It happens because the translators mostly employ established equivalent technique to translate the texts. However, the positive result mentioned previously does not go hand in hand with the quality of the translation. Therefore, to bridge the outcome between the appraisal features of source and target languages reflected through the use of translation techniques and the assessment result of their translation quality, the authors need to generate a holistic SFL-based translation model.

Appraisal in Bilingual Tourism Information Media: Developing an SFL-Based Translation Model