Summary of Athar: a High-quality and Diverse Dataset For Classical Arabic to English Translation, by Mohammed Khalil et al.
ATHAR: A High-Quality and Diverse Dataset for Classical Arabic to English Translation
by Mohammed Khalil, Mohammed Sabry
First submitted to arxiv on: 29 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the scarcity of translation datasets in Classical Arabic, a crucial era for knowledge dissemination. The authors present the ATHAR dataset, comprising 66,000 high-quality Classical Arabic to English translation samples covering various subjects like science, culture, and philosophy. They evaluate the performance of state-of-the-art language models under different settings, highlighting the need for such datasets to fine-tune or incorporate into pretraining pipelines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Classical Arabic is a significant era that enriched knowledge dissemination across communities. The paper presents the ATHAR dataset, which includes 66,000 high-quality Classical Arabic to English translation samples covering various subjects. This dataset can help develop high-quality translation systems and improve language models’ performance. |
Keywords
» Artificial intelligence » Pretraining » Translation
