@inproceedings{huang-asiri-2026-alignar,
    title = "{A}lign{AR}: Generative Sentence Alignment for {A}rabic{--}{E}nglish Parallel Corpora of Legal and Literary Texts",
    author = "Huang, Baorong  and
      Asiri, Ali",
    editor = "Al-Khalifa, Hend  and
      El-Haj, Mo  and
      Ezzini, Saad",
    booktitle = "The 7th Workshop on Open-Source {A}rabic Corpora and Processing Tools ({OSACT}7) with 5 Shared Tasks",
    month = may,
    year = "2026",
    address = "Palma, Mallorca (Spain)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/revision-previews/2026.osact-1.7/",
    doi = "10.63317/2c9daup5j4k7",
    pages = "59--65",
    abstract = "High-quality parallel corpora serve as the fundamental backbone for advancements in Machine Translation (MT) research and the development of effective translation pedagogy. Despite this need, robust resources for the Arabic-English language pair remain significantly scarce. Furthermore, existing datasets are often limited by their reliance on simplistic one-to-one sentence mappings, which fail to capture the structural complexities inherent in natural language translation. To address this deficiency, this paper presents AlignAR, a novel generative sentence alignment method, alongside a comprehensive new Arabic{--}English dataset that juxtaposes simple legal documents with complex literary texts. Our evaluation demonstrates that ``Easy'' datasets lack the discriminatory power to fully assess alignment methods. By reducing one-to-one mappings within our ``Hard'' subset, we exposed the limitations of traditional alignment techniques when faced with structural divergence. In contrast, Large Language Model (LLM) based approaches demonstrated superior robustness and adaptability. Specifically, the proposed LLM-based approaches demonstrated better robustness, achieving an overall F1-score of 85.5{\%}, a nearly 9{\%} improvement over previous methods. This study underscores the importance of complex benchmarks and validates the efficacy of generative models in handling the intricacies of bitext alignment. The codes and datasets are available on Github."
}