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

arXiv: https://arxiv.org/abs/2512.21842 · HTML: https://mlsanity.com/paper/2512.21842/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 13 references that are in the map.
- First posted on arXiv 26 Dec 2025.

## Builds on (most cited first, 2 of 2)

- [SentAlign: Accurate and Scalable Sentence Alignment](https://mlsanity.com/paper/2311.08982/) (2023)
- [Sentence Alignment with Parallel Documents Facilitates Biomedical Machine Translation](https://mlsanity.com/paper/2104.08588/) (2021)

## Built on by (most cited first, 1 of 1)

- [When Do Agents Help? Embedding, LLM and Agentic Alignment of Classical Texts and Their Translations](https://mlsanity.com/paper/2609.33691/) (2026)

## Abstract (arXiv)

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.