Papers by Marwan Torki
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. |
Question Answering Using Hierarchical Attention on Top of BERT Features (D19-58)
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| Challenge: | Recent advances in QA models focus on the targeted area in the passage. |
| Approach: | They propose a model which uses BERT and hierarchical attention to locate a continuous span of the passage that is the answer to the question. |
| Outcome: | The proposed model is based on a BERT embedding and a hierarchical attention model . it can locate a continuous span of the passage that is the answer to the question . |
A Document Descriptor using Covariance of Word Vectors (P18-2)
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| Challenge: | Existing methods for retrieving documents using vectors have been used to model documents and queries using bag-of-words (BOW) representations. |
| Approach: | They propose to use the word embeddings of a document to define a novel document descriptor. |
| Outcome: | The proposed descriptor performs well against state-of-the-art methods in supervised and unsupervised environments. |