Papers by AbdelRahim Elmadany
ORCA: A Challenging Benchmark for Arabic Language Understanding (2023.findings-acl)
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| Challenge: | Despite efforts to evaluate Arabic NLU, no public benchmark of diverse nature exists . a benchmark targeting Arabic needs to take into account that Arabic is not a single language but a collection of languages and language varieties. |
| Approach: | They propose a publicly available benchmark for Arabic language understanding evaluation dubbed ORCA . it covers diverse Arabic varieties and a wide range of Arabic understanding tasks . |
| Outcome: | The proposed benchmark covers Arabic and multilingual models across seven NLU task clusters. |
Mega-COV: A Billion-Scale Dataset of 100+ Languages for COVID-19 (2021.eacl-main)
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Muhammad Abdul-Mageed, AbdelRahim Elmadany, El Moatez Billah Nagoudi, Dinesh Pabbi, Kunal Verma, Rannie Lin
| Challenge: | a global pandemic of coronavirus disease 2019 has impacted millions of people . a human annotation study reveals the utility of our models on a subset of Mega-COV . |
| Approach: | They develop powerful models to analyze tweets related to the pandemic . they use a multilingual Twitter dataset with geo-location information . |
| Outcome: | The proposed model can identify whether a tweet is related to the pandemic and detect misinformation about it. |
Cheetah: Natural Language Generation for 517 African Languages (2024.acl-long)
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| Challenge: | Low-resource African languages pose unique challenges for natural language processing (NLG) We demonstrate the effectiveness of Cheetah through comprehensive evaluations across six generation downstream tasks. |
| Approach: | They develop a multilingual NLG language model for African languages called Cheetah . they demonstrate that Cheethah outperforms other models in six tasks . |
| Outcome: | The proposed model outperforms other models in five of six generation tasks. |
Improving Dialogue Act Classification for Spontaneous Arabic Speech and Instant Messages at Utterance Level (L18-1)
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| Challenge: | Existing methods to detect dialogue act from utterances are limited in Arabic dialects . linguistic knowledge of the speaker is important for understanding spontaneous speech and instant messages . |
| Approach: | They propose a statistical dialogue analysis model to automatically recognize dialogue acts from a textual corpus. |
| Outcome: | The proposed model improves the F-measure by 20% . the proposed model can automatically acquire probabilistic discourse knowledge from a dialogue corpus . |
SERENGETI: Massively Multilingual Language Models for Africa (2023.findings-acl)
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| Challenge: | Pretrained models acquire valuable, generalizable linguistic information during pretraining and have advanced the state of the art on task-specific finetuning. |
| Approach: | They develop a set of massively multilingual language models that covers 517 African languages and language varieties. |
| Outcome: | The proposed models outperform 4 models that cover 4-23 African languages on eight natural language understanding tasks, achieving 82.27 average F_1. |
Dolphin: A Challenging and Diverse Benchmark for Arabic NLG (2023.findings-emnlp)
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| Challenge: | Existing benchmarks for Arabic are limited, but they can be used to measure performance of different languages. |
| Approach: | They propose a benchmark for Arabic that addresses the need for a framework dedicated to Arabic languages and varieties. |
| Outcome: | The proposed benchmark covers 13 different tasks in Arabic and spans 50 test splits. |
Toucan: Many-to-Many Translation for 150 African Language Pairs (2024.findings-acl)
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| Challenge: | We introduce two language models with 1.2 billion and 3.7 billion parameters to improve Machine Translation (MT) for low-resource languages. |
| Approach: | They propose a set of tools to improve Machine Translation (MT) for low-resource languages with a focus on African languages. |
| Outcome: | The proposed model outperforms existing models on MT for African languages and improves translation evaluation metrics for 1K languages including African languages. |
AraT5: Text-to-Text Transformers for Arabic Language Generation (2022.acl-long)
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| Challenge: | Existing models that convert text-based language problems into text-to-text format are not suitable for multilingual tasks. |
| Approach: | They propose a unified Transformer framework that converts all language problems into a text-to-text format. |
| Outcome: | The proposed model performs better on all ARGEN tasks than existing models with 49 less data. |
ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic (2021.acl-long)
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| Challenge: | Pre-trained language models (LMs) are expensive and limited in inference time . a new benchmark for multi-dialectal Arabic language understanding evaluation is developed . |
| Approach: | They introduce two powerful deep bidirectional transformer-based models, ARBERT and MARBERT . they also introduce ARLUE, a new benchmark for multi-dialectal Arabic language understanding evaluation . |
| Outcome: | The proposed models outperform monolingual models with larger vocabulary and larger datasets in Arabic language understanding evaluation. |
AfroLID: A Neural Language Identification Tool for African Languages (2022.emnlp-main)
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| Challenge: | AfroLID is a neural LID toolkit for 517 African languages and varieties. |
| Approach: | They propose to exploit a multi-domain web dataset manually curated from across 14 language families utilizing five orthographic systems to exploit AfroLID. |
| Outcome: | The proposed tool outperforms existing tools on the acutely under-served Twitter domain. |
Toward Micro-Dialect Identification in Diaglossic and Code-Switched Environments (2020.emnlp-main)
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| Challenge: | Existing work on dialect prediction is limited to coarse-grained varieties . a new language model, MARBERT, can predict micro-dialects with 9.9% F1, 76 better than a majority class baseline. |
| Approach: | They propose a new task of Micro-Dialect Identification (MDI) that can predict a fine-grained variety given a single message. |
| Outcome: | The proposed model predicts micro-dialects with 9.9% F1, 76 better than a majority class baseline. |
JASMINE: Arabic GPT Models for Few-Shot Learning (2023.emnlp-main)
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El Moatez Billah Nagoudi, Muhammad Abdul-Mageed, AbdelRahim Elmadany, Alcides Inciarte, Md Tawkat Islam Khondaker
| Challenge: | generative pretraining (GPT) scholarship remains acutely anglocentric, leaving serious gaps in our understanding of the whole class of autoregressive models. |
| Approach: | They propose to use Arabic autoregressive models to evaluate their performance . they use a benchmark to evaluate the models and code for experimenting with them . |
| Outcome: | JASMINE is a suite of powerful Arabic autoregressive models . it shows powerful performance intrinsically and in few-shot learning on a wide range of tasks. |