The Impact of Translingual Meme Transmission on Translation Efficiency and Accuracy in National Regional Studies
Main Article Content
Keywords
transmission of translation memes, translation efficiency and accuracy, regional and country-specific perspective, computer-assisted translation (CAT)
Abstract
This study employs an interdisciplinary research approach that combines computational linguistics, memetics, and translation studies to systematically investigate the impact of cross-linguistic meme transmission on national and regional translation efficiency and accuracy. The primary research methodologies can be divided into four core areas: corpus construction, meme analysis, translation process examination, and efficacy evaluation. Firstly, corpus construction necessitates the collection of a substantial dataset, including memes from various language backgrounds frequently appearing on national and regional social media platforms. Utilizing Web 2.0 tools and application programming interfaces (APIs), this approach facilitates the automated extraction and collation of multilingual data. Preprocessing includes text cleaning, language identification, and the application of natural language processing (NLP) techniques such as tokenization and part-of-speech (POS) tagging to establish a multilingual meme corpus. Secondly, meme analysis involves identifying repetitive patterns, themes, or phrases indicative of meme presence, and categorizing them using advanced algorithms like term frequency-inverse document frequency (TF-IDF) and Latent Dirichlet Allocation (LDA) for topic modeling. Thirdly, the translation process examination comprises both machine translation and manual translation mechanisms and compares them to delineate the differences in handling target language memes. Utilizing technologies such as CAT tools, specifically SDL Trados Studio and MemoQ, this study meticulously investigates the translation workflows to gauge how technological assistance influences translator performance and the fidelity of meme translation. Finally, efficacy evaluation involves both quantitative and qualitative measurements of translation outputs. Quantitative methods include tracking turnaround time and error rates, while qualitative methods rely on back-translation and simultaneous interpretation to compare the retention of ambiguities. Multivariate regression analysis, covariance structure analysis, and variance analysis will be employed to assess the robustness of the research results. This study adopts a multidisciplinary approach, integrating computational methods from data science with theoretical insights from sociolinguistics and regional studies.
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