The inalienability pattern of English and Norwegian

Thunes, Martha

doi:10.15845/bells.v3i1.369

Cited by 1 publication

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“…Thus, MT programs come across many linguistic problems during translation, such as, new words, misspellings and complex words (terms). Thunes (2011) explains that ambiguity in natural language causes many linguistic challenges and he divided ambiguity into three types: lexical ambiguity, structural ambiguity and referential ambiguity. Homonymy, homography and polysemy, for example, pose lexical problems for MT.…”

Section: Literature Reviewmentioning

confidence: 99%

Effectiveness of Machine Translation in Rendering Yemeni Culture-Specific Items into English: Sana'ani Dialect as a Case-in-Point

Khoshafah,

Tagaddeen

2023

jhs

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Undoubtedly, in any given society cultural elements are manifested in all aspects of life. In this globalized age, efforts have been exerted in translating cultural elements. However, since each culture has its own linguistic peculiarities and social aspects, it has become a complicated issue in the field of MT. Despite the recent technological developments, MT still lacks the ability to produce adequate translation in terms of author-based accuracy and reader-oriented cultural acceptability. This study aims to assess the effectiveness of MT in rendering Yemeni culture-specific items into English by comparing the translation outcomes of three MT programs (Google Translate, Yandex Translate and Bing Translator) to find out which program can produce the closest translation to the source text. Moreover, the study aims to identify translation problems of Yemeni culture-specific items resulting from MT. To achieve these aims, the researchers followed a descriptive qualitative method within a comparative framework. The study sample includes 14 Yemeni culture-specific items in their contexts taken from the book Social Issues in Popular Yemeni Culture by Abdu Al Rahman Mutahar and inserted into the three MT systems. The study concluded that MT lacks the ability to translate Yemeni culture-specific items adequately. The three programs failed to render most of the selected sample adequately. Google and Yandex failed to translate 13 cultural items whereas Bing made 12 errors in translating the same cultural items. The study results also showed that MT faced challenges in translating Yemeni culture-specific items, resulting in lexical, semantic and pragmatic problems.

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Section: Literature Reviewmentioning

confidence: 99%