AlignAR: Generative Sentence Alignment for Arabic-English Parallel Corpora of Legal and Literary Texts

• URL: http://arxiv.org/abs/2512.21842v2
• Date: Thu, 01 Jan 2026 02:27:13 GMT
• Title: AlignAR: Generative Sentence Alignment for Arabic-English Parallel Corpora of Legal and Literary Texts
• Authors: Baorong Huang, Ali Asiri,
• Abstract summary: Existing datasets mainly consist of simple one-to-one mappings.<n>We present AlignAR, a generative sentence alignment method, and a new Arabic-English dataset comprising simple legal and complex literary parallel texts.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: High-quality parallel corpora are essential for Machine Translation (MT) research and translation teaching. However, Arabic-English resources remain scarce and existing datasets mainly consist of simple one-to-one mappings. In this paper, we present AlignAR, a generative sentence alignment method, and a new Arabic-English dataset comprising simple legal and complex literary parallel texts. Our evaluation demonstrates that "Easy" datasets lack the discriminatory power to fully assess alignment methods. By reducing one-to-one mappings in our "Hard" subset, we exposed the limitations of traditional alignment methods. In contrast, LLM-based approaches demonstrated better robustness, achieving an overall F1-score of 85.5%, a nearly 9% improvement over previous methods. Our datasets and codes are open-sourced at https://github.com/XXX.

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