暂无分享,去创建一个
Muhammad Abdul-Mageed | El Moatez Billah Nagoudi | AbdelRahim Elmadany | Muhammad Abdul-Mageed | E. Nagoudi | AbdelRahim Elmadany
[1] A. Elnagar,et al. Hotel Arabic-Reviews Dataset Construction for Sentiment Analysis Applications , 2018 .
[2] Jian Zhang,et al. SQuAD: 100,000+ Questions for Machine Comprehension of Text , 2016, EMNLP.
[3] Benjamin Lecouteux,et al. FlauBERT: Unsupervised Language Model Pre-training for French , 2020, LREC.
[4] Michal Perelkiewicz,et al. Pre-training Polish Transformer-based Language Models at Scale , 2020, ICAISC.
[5] Walid Magdy,et al. Overview of OSACT4 Arabic Offensive Language Detection Shared Task , 2020, OSACT.
[6] Anna Rumshisky,et al. A Primer in BERTology: What We Know About How BERT Works , 2020, Transactions of the Association for Computational Linguistics.
[7] Jeffrey Dean,et al. Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.
[8] Richard Socher,et al. The Natural Language Decathlon: Multitask Learning as Question Answering , 2018, ArXiv.
[9] Deniz Yuret,et al. KUISAIL at SemEval-2020 Task 12: BERT-CNN for Offensive Speech Identification in Social Media , 2020, SEMEVAL.
[10] Muhammad Abdul-Mageed,et al. Enabling Deep Learning of Emotion With First-Person Seed Expressions , 2018, PEOPLES@NAACL-HTL.
[11] Thomas Eckart,et al. OSIAN: Open Source International Arabic News Corpus - Preparation and Integration into the CLARIN-infrastructure , 2019, WANLP@ACL 2019.
[12] Motaz Saad,et al. OSAC: Open Source Arabic Corpora , 2010 .
[13] Veselin Stoyanov,et al. Unsupervised Cross-lingual Representation Learning at Scale , 2019, ACL.
[14] Muhammad Abdul-Mageed,et al. Understanding and Detecting Dangerous Speech in Social Media , 2020, OSACT.
[15] Guokun Lai,et al. RACE: Large-scale ReAding Comprehension Dataset From Examinations , 2017, EMNLP.
[16] Kevin Gimpel,et al. ALBERT: A Lite BERT for Self-supervised Learning of Language Representations , 2019, ICLR.
[17] Kareem Darwish,et al. Named Entity Recognition using Cross-lingual Resources: Arabic as an Example , 2013, ACL.
[18] Colin Raffel,et al. mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer , 2021, NAACL.
[19] Muhammad Abdul-Mageed,et al. SAMAR: Subjectivity and sentiment analysis for Arabic social media , 2014, Comput. Speech Lang..
[20] Dat Quoc Nguyen,et al. PhoBERT: Pre-trained language models for Vietnamese , 2020, Findings.
[21] Nizar Habash,et al. The MADAR Shared Task on Arabic Fine-Grained Dialect Identification , 2019, WANLP@ACL 2019.
[22] Saif Mohammad,et al. Sentiment after Translation: A Case-Study on Arabic Social Media Posts , 2015, NAACL.
[23] Saif Mohammad,et al. SemEval-2018 Task 1: Affect in Tweets , 2018, *SEMEVAL.
[24] Omer Levy,et al. GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding , 2018, BlackboxNLP@EMNLP.
[25] Hazem M. Hajj,et al. Multi-Task Learning using AraBert for Offensive Language Detection , 2020, OSACT.
[26] Ahmed Abdelali,et al. ALT Submission for OSACT Shared Task on Offensive Language Detection , 2020, OSACT.
[27] Fatemah Husain,et al. OSACT4 Shared Task on Offensive Language Detection: Intensive Preprocessing-Based Approach , 2020, OSACT.
[28] Muhammad Abdul-Mageed,et al. Deep Models for Arabic Dialect Identification on Benchmarked Data , 2018, VarDial@COLING 2018.
[29] Mark Chen,et al. Language Models are Few-Shot Learners , 2020, NeurIPS.
[30] Ming-Wei Chang,et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.
[31] Ibraheem Tuffaha,et al. Multi-dialect Arabic BERT for Country-level Dialect Identification , 2020, WANLP.
[32] Yan Xu,et al. Incorporating Word and Subword Units in Unsupervised Machine Translation Using Language Model Rescoring , 2019, WMT.
[33] Muhammad Abdul-Mageed,et al. AWATIF: A Multi-Genre Corpus for Modern Standard Arabic Subjectivity and Sentiment Analysis , 2012, LREC.
[34] Martin Malmsten,et al. Playing with Words at the National Library of Sweden - Making a Swedish BERT , 2020, ArXiv.
[35] Hazem Hajj,et al. AraBERT: Transformer-based Model for Arabic Language Understanding , 2020, OSACT.
[36] Colin Raffel,et al. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer , 2019, J. Mach. Learn. Res..
[37] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[38] Mike Schuster,et al. Japanese and Korean voice search , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[39] Jeffrey Pennington,et al. GloVe: Global Vectors for Word Representation , 2014, EMNLP.
[40] AbdelRahim A. Elmadany,et al. ArSAS : An Arabic Speech-Act and Sentiment Corpus of Tweets , 2018 .
[41] Muhammad Abdul-Mageed,et al. Machine Generation and Detection of Arabic Manipulated and Fake News , 2020, WANLP.
[42] Nadir Durrani,et al. Farasa: A Fast and Furious Segmenter for Arabic , 2016, NAACL.
[43] Sanja Fidler,et al. Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[44] Walid Magdy,et al. From Arabic Sentiment Analysis to Sarcasm Detection: The ArSarcasm Dataset , 2020, OSACT.
[45] Omer Levy,et al. SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems , 2019, NeurIPS.
[46] Chris Callison-Burch,et al. Arabic Dialect Identification , 2014, CL.
[47] Murat Can Ganiz,et al. Semantic text classification: A survey of past and recent advances , 2018, Inf. Process. Manag..
[48] Roland Vollgraf,et al. FLAIR: An Easy-to-Use Framework for State-of-the-Art NLP , 2019, NAACL.
[49] Sampo Pyysalo,et al. WikiBERT Models: Deep Transfer Learning for Many Languages , 2020, NODALIDA.
[50] Tomas Mikolov,et al. Advances in Pre-Training Distributed Word Representations , 2017, LREC.
[51] Amir F. Atiya,et al. LABR: A Large Scale Arabic Book Reviews Dataset , 2013, ACL.
[52] Muhammad Abdul-Mageed,et al. Multi-Task Bidirectional Transformer Representations for Irony Detection , 2019, FIRE.
[53] Hend Suliman Al-Khalifa,et al. AraSenTi-Tweet: A Corpus for Arabic Sentiment Analysis of Saudi Tweets , 2017, ACLING.
[54] Khaled Shaalan,et al. Arabic Tweets Sentimental Analysis Using Machine Learning , 2017, IEA/AIE.
[55] George Kurian,et al. Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation , 2016, ArXiv.
[56] Paolo Rosso,et al. IDAT at FIRE2019: Overview of the Track on Irony Detection in Arabic Tweets , 2019, FIRE.
[57] Yassine Benajiba,et al. ANERsys 2.0: Conquering the NER Task for the Arabic Language by Combining the Maximum Entropy with POS-tag Information , 2007, IICAI.
[58] Khaled Shaalan,et al. Self-Training Pre-Trained Language Models for Zero- and Few-Shot Multi-Dialectal Arabic Sequence Labeling , 2021, EACL.
[59] Laurent Romary,et al. CamemBERT: a Tasty French Language Model , 2019, ACL.
[60] Omer Levy,et al. RoBERTa: A Robustly Optimized BERT Pretraining Approach , 2019, ArXiv.
[61] Amir F. Atiya,et al. ASTD: Arabic Sentiment Tweets Dataset , 2015, EMNLP.
[62] Sebastian Riedel,et al. MLQA: Evaluating Cross-lingual Extractive Question Answering , 2019, ACL.
[63] Alexander Erdmann,et al. CAMeL Tools: An Open Source Python Toolkit for Arabic Natural Language Processing , 2020, LREC.
[64] Ahmed Khoumsi,et al. Weighted combination of BERT and N-GRAM features for Nuanced Arabic Dialect Identification , 2020, WANLP.
[65] Kemal Oflazer,et al. The MADAR Arabic Dialect Corpus and Lexicon , 2018, LREC.
[66] Roland Vollgraf,et al. Contextual String Embeddings for Sequence Labeling , 2018, COLING.
[67] Nizar Habash,et al. NADI 2021: The Second Nuanced Arabic Dialect Identification Shared Task , 2021, WANLP.
[68] Danqi Chen,et al. of the Association for Computational Linguistics: , 2001 .
[69] Hazem M. Hajj,et al. ArSentD-LEV: A Multi-Topic Corpus for Target-based Sentiment Analysis in Arabic Levantine Tweets , 2019, ArXiv.
[70] Graham Neubig,et al. XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization , 2020, ICML.
[71] Tapio Salakoski,et al. Multilingual is not enough: BERT for Finnish , 2019, ArXiv.
[72] Khaled Shaalan,et al. Character convolutions for Arabic Named Entity Recognition with Long Short-Term Memory Networks , 2019, Comput. Speech Lang..
[73] Tommaso Caselli,et al. BERTje: A Dutch BERT Model , 2019, ArXiv.
[74] Wajdi Zaghouani,et al. Arap-Tweet: A Large Multi-Dialect Twitter Corpus for Gender, Age and Language Variety Identification , 2018, LREC.
[75] Saif Mohammad,et al. SemEval-2016 Task 7: Determining Sentiment Intensity of English and Arabic Phrases , 2016, *SEMEVAL.
[76] Ahmed Abdelali,et al. QADI: Arabic Dialect Identification in the Wild , 2020, WANLP.
[77] Benoît Sagot,et al. Asynchronous Pipeline for Processing Huge Corpora on Medium to Low Resource Infrastructures , 2019 .
[78] Walid Magdy,et al. Mazajak: An Online Arabic Sentiment Analyser , 2019, WANLP@ACL 2019.
[79] Muhammad Abdul-Mageed,et al. AraNet: A Deep Learning Toolkit for Arabic Social Media , 2020, OSACT.
[80] Nizar Habash,et al. NADI 2020: The First Nuanced Arabic Dialect Identification Shared Task , 2020, WANLP.
[81] Muhammad Abdul-Mageed,et al. No Army, No Navy: BERT Semi-Supervised Learning of Arabic Dialects , 2019, WANLP@ACL 2019.
[82] Hazem M. Hajj,et al. Neural Arabic Question Answering , 2019, WANLP@ACL 2019.
[83] Luke S. Zettlemoyer,et al. Deep Contextualized Word Representations , 2018, NAACL.
[84] Bilel Elayeb,et al. ANT Corpus: An Arabic News Text Collection for Textual Classification , 2017, 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA).
[85] Preslav Nakov,et al. SemEval-2016 Task 4: Sentiment Analysis in Twitter , 2016, *SEMEVAL.
[86] Kamel Smaïli,et al. Evaluation of Topic Identification Methods on Arabic Corpora , 2011, J. Digit. Inf. Manag..