ArzEn-MultiGenre: An aligned parallel dataset of Egyptian Arabic song lyrics, novels, and subtitles, with English translations
This provides a new resource for researchers and practitioners in machine translation and linguistics, but it is incremental as it expands existing datasets with new genres.
The authors tackled the lack of diverse parallel data for Egyptian Arabic by creating ArzEn-MultiGenre, a manually translated and aligned dataset of 25,557 segment pairs from song lyrics, novels, and subtitles, which can be used to benchmark machine translation models and support various research and practical applications.
ArzEn-MultiGenre is a parallel dataset of Egyptian Arabic song lyrics, novels, and TV show subtitles that are manually translated and aligned with their English counterparts. The dataset contains 25,557 segment pairs that can be used to benchmark new machine translation models, fine-tune large language models in few-shot settings, and adapt commercial machine translation applications such as Google Translate. Additionally, the dataset is a valuable resource for research in various disciplines, including translation studies, cross-linguistic analysis, and lexical semantics. The dataset can also serve pedagogical purposes by training translation students and aid professional translators as a translation memory. The contributions are twofold: first, the dataset features textual genres not found in existing parallel Egyptian Arabic and English datasets, and second, it is a gold-standard dataset that has been translated and aligned by human experts.