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1
AraBART: a Pretrained Arabic Sequence-to-Sequence Model for Abstractive Summarization ...
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2
NADI 2021: The Second Nuanced Arabic Dialect Identification Shared Task ...
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3
The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models ...
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4
Universal Dependencies 2.9
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021
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5
Universal Dependencies 2.8.1
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021
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6
Universal Dependencies 2.8
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021
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7
Language Models in Sociological Research: An Application to Classifying Large Administrative Data and Measuring Religiosity ...
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8
Language Models in Sociological Research: An Application to Classifying Large Administrative Data and Measuring Religiosity ...
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9
Morphosyntactic Tagging with Pre-trained Language Models for Arabic and its Dialects ...
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10
Automatic Error Type Annotation for Arabic ...
Abstract: We present ARETA, an automatic error type annotation system for Modern Standard Arabic. We design ARETA to address Arabic’s morphological richness and orthographic ambiguity. We base our error taxonomy on the Arabic Learner Corpus (ALC) Error Tagset with some modifications. ARETA achieves a performance of 85.8% (micro average F1 score) on a manually annotated blind test portion of ALC. We also demonstrate ARETA’s usability by applying it to a number of submissions from the QALB 2014 shared task for Arabic grammatical error correction. The resulting analyses give helpful insights on the strengths and weaknesses of different submissions, which is more useful than the opaque M2 scoring metrics used in the shared task. ARETA employs a large Arabic morphological analyzer but is completely unsupervised otherwise. We make ARETA publicly available. ...
Keyword: Computational Linguistics; Information Extraction; Language Models; Machine Learning; Machine Learning and Data Mining; Natural Language Processing
URL: https://dx.doi.org/10.48448/1s26-nw85
https://underline.io/lecture/39891-automatic-error-type-annotation-for-arabic
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11
Multitask Easy-First Dependency Parsing: Exploiting Complementarities of Different Dependency Representations
In: Proceedings of the 28th International Conference on Computational Linguistics ; 28th International Conference on Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-03168039 ; 28th International Conference on Computational Linguistics, Dec 2020, Barcelona (on line), Spain. ⟨10.18653/v1/2020.coling-main.225⟩ (2020)
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12
NADI 2020: The First Nuanced Arabic Dialect Identification Shared Task ...
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13
Universal Dependencies 2.7
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020
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14
Universal Dependencies 2.6
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020
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15
A Panoramic Survey of Natural Language Processing in the Arab World ...
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16
The Paradigm Discovery Problem ...
Erdmann, Alexander; Elsner, Micha; Wu, Shijie. - : ETH Zurich, 2020
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17
NADI 2020: The First Nuanced Arabic Dialect Identification Shared Task ...
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18
An Online Readability Leveled Arabic Thesaurus ...
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19
Gender-Aware Reinflectionusing Linguistically Enhanced Neural Models ...
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20
The Paradigm Discovery Problem
In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)
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