As a burgeoning area of interdisciplinary enquiry, linguistic landscape (LL) research can shed light on the sociopolitical, cultural and demographical realities of a particular locale. However, LL research has seldom explored major international cities from a translation and contrastive perspective. Drawing on a corpus containing 450 photographs (e.g. shop fronts and public signs), this study investigates the multilingual landscape involving the Arabic-English pair in Dubai, an international hub representing a vivid case of micro-cosmopolitanism and superdiversity in the 21st century. An examination of the bilingual and translation practices enacted on Dubai’s LL points to a ubiquitous phenomenon that the Arabic information is often not authentic Arabic but transliterations from English (pseudo Arabic in disguise). Such use of transliteration privileges the phonetic transference of sounds, at the expense of meaning and function. The prevalent use of transliteration as a ‘go-to’ strategy is interesting, considering the obvious existence of pure Arabic equivalents. To provide some ethnographic context for the analysis, 10 people in Dubai were interviewed (Arabic speakers from different Arab countries) to establish whether the transliterated Arabic can be understood and the possible rationale behind this interesting linguistic decision. Such symbolic and decorative use of Arabic reflects Dubai’s global city status with immigrants significantly outnumbering the indigenous Arabic-speaking natives. The widespread aesthetic use of ‘Arabised English’ points to the influence of English in a globalised world. Some tentative reasons are provided to explain the phenomenon.
Machine translation (MT) has made significant strides and has reached accuracy levels that often make the post-editing (PE) of MT output a viable alternative to manual translation. However, despite professional translators increasingly considering PE as a valid stage in their translation workflow, little has been done to investigate MT output for the purpose of informing training in PE. Against this background, the present project focuses on the handling of tense and aspect configurations in the English translation of Arabic sentences using current neural machine translation (NMT) systems. Using a dataset of representative Arabic sentences, the output of five NMT engines was assessed against reference translations. The investigation reveals regressing accuracy levels when comparing morphological, structural, and contextual tenses. These findings are believed to represent valuable information that contributes to a more informed training in the PE of Arabic-into-English NMT output.
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