A ra S im: Optimizing A rabic Dialect Translation in Children’s Literature with LLM s and Similarity Scores (2025)

Abstract: The goal of the paper is to address the linguistic gap faced by young Egyptian Arabic speakers through translating children stories from Modern Standard Arabic to the Egyptian Cairo dialect. Claude is used for initial translation, and a fine-tuned AraT5 model is used for backtranslation. The translation quality is assessed using semantic similarity and BLUE scores to compare the original texts and the translations. The resulting corpus contains 130 stories which were revised by native Egyptian speakers who are professional translators. The strengths of this paper are multiple: working on a less-resourced variety, addressing an important social issue, creating a dataset with potential real-life applications, and ensuring the quality of the produced dataset through human validation.

## background research translating between modern standard arabic (msa) and regional dialects poses significant challenges due to linguistic variations and limited annotated corpora @xcite . various approaches have been employed, including rule-based methods, statistical machine translation (smt), and neural machine translation (nmt) @xcite . while rule-based and smt methods have provided foundational insights, they have limitations in capturing dialectal nuances. nmt, particularly transformer models like arat5 @xcite , has emerged as a promising approach due to its ability to handle long-range dependencies and complex linguistic structures. ## design and methodology this study aims to translate children's stories from modern standard arabic (msa) to the egyptian dialect, leveraging advanced neural machine translation models. the primary objectives are to evaluate the performance of these models, improve their translation quality through fine-tuning, and create a high-quality parallel corpus for future research. ## experimental results and discussion this section presents the findings from the translation experiments and fine-tuning processes using the claude and arat5 models. the results are evaluated using semantic similarity and bleu scores to assess the quality and accuracy of translations from modern standard arabic (msa) to the egyptian (cairo) dialect. ## conclusion this study has made significant progress in addressing the complex challenge of translating children's stories from modern standard arabic (msa) to the egyptian (cairo) dialect using advanced neural machine translation models. the study's methodology combining initial translations with claude and fine-tuning the arat5 model, has yielded marked improvements in translation quality, as evidenced by higher similarity and bleu scores. the basic arat5 model achieved an average semantic similarity score of 0.945 and through fine-tuning on an egyptian (cairo) dialectspecific dataset, we were able to enhance this alignment, achieving an average semantic similarity score of 0.971. the 2.6% improvement demonstrates the model's enhanced capability to understand and replicate the meanings and linguistic characteristics specific to the egyptian (cairo) dialect, further bridging the gap between msa and the dialect used in everyday communication by egyptian children. furthermore, the bleu score, which measures the precision of the translated output by comparing it to reference translations, also showed notable improvement by increasing from 0.0828 to 0.0867 after fine-tuning. this enhancement highlighted the fine-tuned model's ability to produce translations that are not only more semantically accurate but also more aligned with human linguistic expression. another achievement of this study is the creation of a comprehensive parallel corpus comprising 130 children's stories in both msa and egyptian dialect. this corpus stands to benefit future studies and model training efforts in the field of arabic dialect translation. the study's evaluation framework, incorporating both semantic similarity and bleu scores, along with manual reviews, ensured a thorough assessment of translation quality while maintaining cultural and contextual accuracy. this approach has resulted in translations that are culturally resonant and linguistically. additionally, this research contributes to enhancing the accessibility of children's literature for young arabic speakers. by providing stories in a more familiar dialect, opening new avenues for educational and cultural engagement. in conclusion, this research represents a significant step forward in arabic dialect translation. its findings and resources contribute to further advancements in this crucial area of arabic language processing and cultural preservation.