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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 ...
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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)
Abstract: International audience ; We present a parsing model for projective dependency trees which takes advantage of the existence of complementary dependency annotations for a language. This is the case for Arabic with the availability of CATiB and UD treebanks. Our system performs syntactic parsing according to both annotation types jointly as a sequence of arc-creating operations following the Easy-First approach, and partially created trees for one annotation type are also available to the other as features for the score function. This method gives error reduction of 9.9% on CATiB and 6.1% on UD compared to a single-task baseline, and ablation tests show that the main contribution of this reduction is given by sharing tree representation between tasks, and not simply sharing BiLSTM layers as is usually performed in NLP multitask systems.
Keyword: [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing; [INFO]Computer Science [cs]
URL: https://doi.org/10.18653/v1/2020.coling-main.225
https://hal.archives-ouvertes.fr/hal-03168039
https://hal.archives-ouvertes.fr/hal-03168039/document
https://hal.archives-ouvertes.fr/hal-03168039/file/2020.coling-main.225.pdf
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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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