Papers by Mahmoud Yusuf
Arabic Dialect Identification with a Few Labeled Examples Using Generative Adversarial Networks (2022.aacl-main)
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| Challenge: | Experimental results show that transformer-based models can handle Dialect Arabic (DA) classification tasks with a large corpus of labeled examples. |
| Approach: | They extend transformer-based models with unlabeled data in a generative adversarial setting using semi-supervised Generative Adversarial Networks (SS-GAN) they show that the model can produce high-quality embeddings for the Dialect Arabic examples and generalize for the downstream classification task given few labeled examples. |
| Outcome: | The proposed model outperforms models with unlabeled data in a generative adversarial setting with unlabelled examples and faster convergence when only a few labeled examples are available. |