Literary Translations and Synthetic Data for Machine Translation of Low-resourced M iddle E astern Languages (2025)

Abstract: Middle Eastern languages represent a linguistically diverse landscape, yet few have received substantial attention in language and speech technology outside those with official status. Machine translation, a cornerstone application in computational linguistics, remains particularly underexplored for these predominantly non-standardized, spoken varieties. This paper proposes data alignment and augmentation techniques that leverage monolingual corpora and large language models to create high-quality parallel corpora for low-resource Middle Eastern languages. Through systematic fine-tuning of a pretrained machine translation model in a multilingual framework, our results demonstrate that corpus quality consistently outperforms quantity as a determinant of translation accuracy. Furthermore, we provide empirical evidence that strategic data selection significantly enhances cross-lingual transfer in multilingual translation systems. These findings offer valuable insights for developing machine translation solutions in linguistically diverse, resource-constrained environments.

## introduction machine translation (mt) represents one of the most transformative applications in natural language processing (nlp), driving numerous breakthrough discoveries in the field. the evolution of mt has progressed from rule-based techniques to sophisticated deep learning approaches and, most recently, to large language models (llms) @xcite . despite these paradigm shifts, data availability remains the fundamental constraint, leaving mt far from solved for low-resourced and under-represented languages and varieties. of particular interest to this paper are such languages in the middle east-a region with rich linguistic heterogeneity. many languages in the middle east lack formal status or standardization, face sociopolitical marginalization, and are systematically disadvantaged in technological development. consequently, these languages have not benefited equitably from recent advances in mt technology, widening the digital language divide. in our previous work, parme-described in detail in @xcite , we explored a participatory research initiative where native speakers contribute to translating sentences into eight middle eastern languages: luri bakhtiari, laki kurdish, gilaki, hawrami, mazandarani, southern kurdish, talysh, and zazaki. collecting data in a context where spoken tradition predominates over writing presents significant challenges. this effort resulted in over 36,000 translations, which were used to fine-tune the no language left behind (nllb) pretrained translation model @xcite . our previous experiments yield bleu scores ranging from 2.89 to 16.54, indicating substantial room for improvement. the current paper expands on our previous data collection approach through two complementary approaches illustrated in figure 1 . in the first approach, we leverage literary works by aligning sentences from translated works in the selected languages to the original english texts, using both manual and automated alignment tech-niques. in the second approach, we extract sentences from monolingual corpora and translate them using gemini-2.0-flash, creating synthetic parallel data. using these datasets, we then systematically evaluate how these various data sources affect the performance of fine-tuned multilingual translation models. our findings reveal that incorporating these new datasets improves model performance overall, but with an important caveat: increasing data quantity for one language can sometimes adversely affect performance for others in a multilingual setting. this highlights the complex interplay between data quantity, quality, and distribution in multilingual mt systems for lowresource languages. ## methodology complementary to parme @xcite , our previous participatory research where english sentences are translated by experts into one of the selected languages, we explore bitext mining and llm-based data augmentation to further extract parallel sentences. ## experiments 4.1 experimental setup to adapt a multilingual model for our target languages, we leverage nllb (600m variant) by systematically integrating embeddings from related languages through a structured token-based approach. this integration follows two key steps. first, we expanded the tokenizer's vocabulary by introducing language-specific tokens (e.g., zza_latn for zazaki) while preserving the existing language tokens. second, we initialize embeddings for these new tokens by borrowing from phylogenetically related languages: central kurdish embeddings for hawrami, laki, and southern kurdish; northern kurdish for zazaki; and farsi for luri bakhtiari, gilaki, mazandarani, and talysh. for evaluation consistency, we utilize the standardized test sets from parme, each containing around 1,000 sentences per language in a single orthography. these test sets maintain representativeness across the non-standardized linguistic landscape by incorporating a uniform distribution of dialectal variations. we conduct @xmath0, e.g., pl for merging parme and llm-based datasets. we evaluate the performance using bleu metric in sacrebleu @xcite . 3 our baseline represents the highest bleu score achieved by nllb prior to fine-tuning. for fine-tuning, we employ a batch size of 8 with 4-step gradient accumulation, a conservative learning rate of 3e-5, and trained for 20 epochs with 0.1 warmup. both source and target sequences were truncated to 128 tokens, and we implemented beam search with a beam size of 5 during inference. training was conducted on nvidia rtx 3090 gpus (24gb vram) with completion times of 9.4 to 16.1 hours per model. ## conclusion and discussion this paper sheds light on eight low-resourced middle eastern languages by fine-tuning a pretrained mt model using different sources of data, from manually translated and aligned sentences to automatically aligned and automatically-translated ones. our experiments demonstrate three key findings. first, data quality consistently outperforms quantity as a determinant of translation accuracy, with the manually aligned (m) data providing the most substantial improvements despite its relatively smaller size. second, we observed complex cross-linguistic transfer effects where adding data for one language sometimes adversely affects performance for others, highlighting the importance of strategic dataset selection in multilingual systems. third, we found significant performance variations across dialectal varieties within the same language. while our models perform well on all languages in comparison to the baseline, achieving 15.46 bleu score for hawrami at the highest, there remains substantial room for improvement. limitations despite these advances, our work has several limitations. first, we explored only a limited set of open-weight llms for data augmentation; future work could investigate a broader range of models, such as madlad-400 @xcite and mistral @xcite , and in-context learning strategies. second, our automatic alignment approach relies on embeddings from closely-related languages, which could be improved by training or fine-tuning embeddings on monolingual data of our selected languages. third, our data augmentation techniques could be expanded to include synthetic data generation using bilingual lexicon induction, morphological variations, and back-translation methods. finally, unlike the test sets that are uniform in orthography, our collected data for training and validation are composed of more than one orthography, as in hawrami, zazaki and gilaki. given that normalization and transliteration of these orthographies are not trivial, future work can also study the effect of orthographical variation on mt. ethics statement our data collection process adhered to rigorous ethical standards with careful attention to fairness and representation. while we maintained comprehensive inclusion criteria appropriate for low-resource language documentation, we acknowledge that the literary nature of our corpus means some character dialogue may contain language that reflects historical or cultural contexts that modern readers might find objectionable. all materials were obtained through formal agreements with publishers and translators, with appropriate intellectual property permissions secured. contributors received fair compensation for their work, and their contributions are explicitly acknowledged. our research prioritizes expanding nlp for underrepresented languages while maintaining responsible data stewardship practices.