ATHAR: A High-Quality and Diverse Dataset for Classical Arabic to English Translation
This addresses a data bottleneck for researchers and developers working on translation systems for Classical Arabic literature, though it is incremental as it provides a new dataset rather than a novel method.
The authors tackled the scarcity of high-quality translation datasets for Classical Arabic to English by creating the ATHAR dataset with 66,000 samples across diverse topics, and found that current state-of-the-art LLMs need such datasets for improved performance through fine-tuning or pretraining.
Classical Arabic represents a significant era that encompasses the golden age of Arab culture, philosophy, and scientific literature. With a broad consensus on the importance of translating these literatures to enrich knowledge dissemination across communities, the advent of large language models (LLMs) and translation systems offers promising tools to facilitate this goal. However, we have identified a scarcity of translation datasets in Classical Arabic, which are often limited in scope and topics, hindering the development of high-quality translation systems. In response, we present the ATHAR dataset, which comprises 66,000 high-quality classical Arabic to English translation samples that cover a wide array of topics including science, culture, and philosophy. Furthermore, we assess the performance of current state-of-the-art LLMs under various settings, concluding that there is a need for such datasets in current systems. Our findings highlight how models can benefit from fine-tuning or incorporating this dataset into their pretraining pipelines. The dataset is publicly available on the HuggingFace Data Hub: https://huggingface.co/datasets/mohamed-khalil/ATHAR.