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ALEF: An Enhanced Approach to Arabic-English Bilingual Translation
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href="https://publications.waset.org/abstracts/search?q=Sonu%20Kumar"> Sonu Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=Sundar%20Ali"> Sundar Ali</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Owais%20Mahmood"> Muhammad Owais Mahmood</a>, <a href="https://publications.waset.org/abstracts/search?q=Areeb%20Ur%20Rehman"> Areeb Ur Rehman</a>, <a href="https://publications.waset.org/abstracts/search?q=Bahram%20Baloch"> Bahram Baloch</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Accurate translation between structurally diverse languages, such as Arabic and English, presents a critical challenge in natural language processing due to significant linguistic and cultural differences. This paper investigates the effectiveness of Facebook鈥檚 mBART model, fine-tuned specifically for sequence-tosequence (seq2seq) translation tasks between Arabic and English, and enhanced through advanced refinement techniques. Our approach leverages the Alef Dataset, a meticulously curated parallel corpus spanning various domains to capture the linguistic richness, nuances, and contextual accuracy essential for high-quality translation. We further refine the model鈥檚 output using advanced language models such as GPT-3.5 and GPT-4, which improve fluency, coherence, and correct grammatical errors in translated texts. The fine-tuned model demonstrates substantial improvements, achieving a BLEU score of 38.97, METEOR score of 58.11, and TER score of 56.33, surpassing widely used systems such as Google Translate. These results underscore the potential of mBART, combined with refinement strategies, to bridge the translation gap between Arabic and English, providing a reliable, context-aware machine translation solution that is robust across diverse linguistic contexts. <iframe src="https://publications.waset.org/abstracts/193289.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title="natural language processing">natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20translation" title=" machine translation"> machine translation</a>, <a href="https://publications.waset.org/abstracts/search?q=fine-tuning" title=" fine-tuning"> fine-tuning</a>, <a href="https://publications.waset.org/abstracts/search?q=Arabic-English%20translation" title=" Arabic-English translation"> Arabic-English translation</a>, <a href="https://publications.waset.org/abstracts/search?q=transformer%20models" title=" transformer models"> transformer models</a>, <a href="https://publications.waset.org/abstracts/search?q=seq2seq%20translation" title=" seq2seq translation"> seq2seq translation</a>, <a href="https://publications.waset.org/abstracts/search?q=translation%20evaluation%20metrics" title=" translation evaluation metrics"> translation evaluation metrics</a>, <a href="https://publications.waset.org/abstracts/search?q=cross-linguistic%20communication" title=" cross-linguistic communication"> cross-linguistic communication</a> </p> <a href="https://publications.waset.org/abstracts/193289/alef-an-enhanced-approach-to-arabic-english-bilingual-translation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/193289.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge 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