This paper introduces a supervised approach for performing sentence level dialect identification between Modern Standard Arabic and Egyptian Dialectal Arabic. We use token level labels to derive sentence-level features. These features are then used with other core and meta features to train a generative classifier that predicts the correct label for each sentence in the given input text. The system achieves an accuracy of 85.5% on an Arabic online-commentary dataset outperforming a previously proposed approach achieving 80.9% and reflecting a significant gain over a majority baseline of 51.9% and two strong baseline systems of 78.5% and 80.4%, respectively.
Another work presents a supervised approach for performing sentence level dialect identification between Modern Standard Arabic and Egyptian Dialectal Arabic REF ).
Sentence Level Dialect Identification in Arabic
ACL
Computer Science