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AraBART: a Pretrained Arabic Sequence-to-Sequence Model for Abstractive Summarization ...
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NADI 2021: The Second Nuanced Arabic Dialect Identification Shared Task ...
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The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models ...
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Morphosyntactic Tagging with Pre-trained Language Models for Arabic and its Dialects ...
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NADI 2020: The First Nuanced Arabic Dialect Identification Shared Task ...
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A Panoramic Survey of Natural Language Processing in the Arab World ...
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Adversarial Multitask Learning for Joint Multi-Feature and Multi-Dialect Morphological Modeling ...
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Joint Diacritization, Lemmatization, Normalization, and Fine-Grained Morphological Tagging ...
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Universal Dependencies 2.2
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In: https://hal.archives-ouvertes.fr/hal-01930733 ; 2018 (2018)
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MADARi: A Web Interface for Joint Arabic Morphological Annotation and Spelling Correction ...
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Utilizing Character and Word Embeddings for Text Normalization with Sequence-to-Sequence Models ...
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Universal Dependencies 2.1
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In: https://hal.inria.fr/hal-01682188 ; 2017 (2017)
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Low Resourced Machine Translation via Morpho-syntactic Modeling: The Case of Dialectal Arabic ...
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Egyptian Arabic to English Statistical Machine Translation System for NIST OpenMT'2015 ...
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Sajjad, Hassan; Durrani, Nadir; Guzman, Francisco; Nakov, Preslav; Abdelali, Ahmed; Vogel, Stephan; Salloum, Wael; Kholy, Ahmed El; Habash, Nizar. - : arXiv, 2016
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Abstract:
The paper describes the Egyptian Arabic-to-English statistical machine translation (SMT) system that the QCRI-Columbia-NYUAD (QCN) group submitted to the NIST OpenMT'2015 competition. The competition focused on informal dialectal Arabic, as used in SMS, chat, and speech. Thus, our efforts focused on processing and standardizing Arabic, e.g., using tools such as 3arrib and MADAMIRA. We further trained a phrase-based SMT system using state-of-the-art features and components such as operation sequence model, class-based language model, sparse features, neural network joint model, genre-based hierarchically-interpolated language model, unsupervised transliteration mining, phrase-table merging, and hypothesis combination. Our system ranked second on all three genres. ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://dx.doi.org/10.48550/arxiv.1606.05759 https://arxiv.org/abs/1606.05759
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Conventional Orthography for Dialectal Arabic (CODA): Principles and Guidelines -- Egyptian Arabic - Version 0.7 - March 2012
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Conventional Orthography for Dialectal Arabic (CODA): Principles and Guidelines -- Egyptian Arabic - Version 0.7 - March 2012 ...
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Annotation Guidelines for Arabic Nominal Gender, Number, and Rationality
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LDC Arabic Treebanks and Associated Corpora: Data Divisions Manual
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LDC Arabic Treebanks and Associated Corpora: Data Divisions Manual ...
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