AlignAR: LLM-Based Sentence Alignment for Arabic-English Parallel Corpora
Analysis
This paper addresses the scarcity of high-quality Arabic-English parallel corpora, crucial for machine translation and translation education. It introduces AlignAR, a generative sentence alignment method, and a new dataset focusing on complex legal and literary texts. The key contribution is the demonstration of LLM-based approaches' superior performance compared to traditional methods, especially on a 'Hard' subset designed to challenge alignment algorithms. The open-sourcing of the dataset and code is also a significant contribution.
Key Takeaways
- •Addresses the lack of high-quality Arabic-English parallel corpora.
- •Introduces AlignAR, a generative sentence alignment method.
- •Presents a new dataset with complex legal and literary texts.
- •Demonstrates the superior performance of LLM-based alignment methods.
- •Highlights the limitations of traditional alignment methods on challenging datasets.
- •Open-sources the dataset and code.
Reference / Citation
View Original"LLM-based approaches demonstrated superior robustness, achieving an overall F1-score of 85.5%, a 9% improvement over previous methods."