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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 9 of 9 1 Investigating Code-Mixed Modern Standard Arabic-Egyptian to English Machine Translation ... Nagoudi, El Moatez Billah; Elmadany, AbdelRahim; Abdul-Mageed, Muhammad. - : arXiv, 2021 BASE Show details 2 NADI 2021: The Second Nuanced Arabic Dialect Identification Shared Task ... Abdul-Mageed, Muhammad; Zhang, Chiyu; Elmadany, AbdelRahim. - : arXiv, 2021 BASE Show details 3 ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic ... Abdul-Mageed, Muhammad; Elmadany, AbdelRahim; Nagoudi, El Moatez Billah. - : arXiv, 2021 BASE Show details 4 AraT5: Text-to-Text Transformers for Arabic Language Generation ... Nagoudi, El Moatez Billah; Elmadany, AbdelRahim; Abdul-Mageed, Muhammad. - : arXiv, 2021 BASE Show details 5 DiaLex: A Benchmark for Evaluating Multidialectal Arabic Word Embeddings ... Abdul-Mageed, Muhammad; Elbassuoni, Shady; Doughman, Jad. - : arXiv, 2020 BASE Show details 6 Mega-COV: A Billion-Scale Dataset of 100+ Languages for COVID-19 ... Abdul-Mageed, Muhammad; Elmadany, AbdelRahim; Nagoudi, El Moatez Billah; Pabbi, Dinesh; Verma, Kunal; Lin, Rannie. - : arXiv, 2020 Abstract: We describe Mega-COV, a billion-scale dataset from Twitter for studying COVID-19. The dataset is diverse (covers 268 countries), longitudinal (goes as back as 2007), multilingual (comes in 100+ languages), and has a significant number of location-tagged tweets (~169M tweets). We release tweet IDs from the dataset. We also develop and release two powerful models, one for identifying whether or not a tweet is related to the pandemic (best F1=97%) and another for detecting misinformation about COVID-19 (best F1=92%). A human annotation study reveals the utility of our models on a subset of Mega-COV. Our data and models can be useful for studying a wide host of phenomena related to the pandemic. Mega-COV and our models are publicly available. ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences; Social and Information Networks cs.SI URL: https://arxiv.org/abs/2005.06012 https://dx.doi.org/10.48550/arxiv.2005.06012 BASE Hide details 7 Toward Micro-Dialect Identification in Diaglossic and Code-Switched Environments ... Abdul-Mageed, Muhammad; Zhang, Chiyu; Elmadany, AbdelRahim. - : arXiv, 2020 BASE Show details 8 DiaNet: BERT and Hierarchical Attention Multi-Task Learning of Fine-Grained Dialect ... Abdul-Mageed, Muhammad; Zhang, Chiyu; Elmadany, AbdelRahim. - : arXiv, 2019 BASE Show details 9 Improving Dialogue Act Classification for Spontaneous Arabic Speech and Instant Messages at Utterance Level ... Elmadany, AbdelRahim; Abdou, Sherif; Gheith, Mervat. - : arXiv, 2018 BASE Show details

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