Towards the Development of Balanced Synthetic Data for Correcting Grammatical Errors in Arabic: An Approach Based on Error Tagging Model and Synthetic Data Generating Model | Paper Citation Record

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This paper cites A multilayer convolutional encoder-decoder neural network for grammati- cal error correction.

Towards the Development of Balanced Synthetic Data for Correcting Grammatical Errors in Arabic: An Approach Based on Error Tagging Model and Synthetic Data Generating Model A multilayer convolutional encoder-decoder neural network for grammati- cal error correction

Reference 1

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This paper cites Approaching Neural Grammatical Error Correction as a Low-Resource Machine Translation Task.

Towards the Development of Balanced Synthetic Data for Correcting Grammatical Errors in Arabic: An Approach Based on Error Tagging Model and Synthetic Data Generating Model Approaching Neural Grammatical Error Correction as a Low-Resource Machine Translation Task

Reference 2

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This paper cites Challenges in arabic natural language processing.

Towards the Development of Balanced Synthetic Data for Correcting Grammatical Errors in Arabic: An Approach Based on Error Tagging Model and Synthetic Data Generating Model Challenges in arabic natural language processing

Reference 3

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Towards the Development of Balanced Synthetic Data for Correcting Grammatical Errors in Arabic: An Approach Based on Error Tagging Model and Synthetic Data Generating Model The first qalb shared task on automatic text correction for arabic

Reference 4

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Towards the Development of Balanced Synthetic Data for Correcting Grammatical Errors in Arabic: An Approach Based on Error Tagging Model and Synthetic Data Generating Model The second qalb shared task on automatic text correction for arabic

Reference 5

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This paper cites Cmuq@ qalb-2014: An smt-based system for automatic arabic error correction.

Towards the Development of Balanced Synthetic Data for Correcting Grammatical Errors in Arabic: An Approach Based on Error Tagging Model and Synthetic Data Generating Model Cmuq@ qalb-2014: An smt-based system for automatic arabic error correction

Reference 6

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This paper cites A web-based annotation framework for large-scale text correction.

Towards the Development of Balanced Synthetic Data for Correcting Grammatical Errors in Arabic: An Approach Based on Error Tagging Model and Synthetic Data Generating Model A web-based annotation framework for large-scale text correction

Reference 7

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This paper cites Synthetic data with neural machine translation for automatic correction in arabic grammar.

Towards the Development of Balanced Synthetic Data for Correcting Grammatical Errors in Arabic: An Approach Based on Error Tagging Model and Synthetic Data Generating Model Synthetic data with neural machine translation for automatic correction in arabic grammar

Reference 8

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This paper cites Automatic arabic grammatical error correction based on expectation-maximization routing and target-bidirectional agreement.

Towards the Development of Balanced Synthetic Data for Correcting Grammatical Errors in Arabic: An Approach Based on Error Tagging Model and Synthetic Data Generating Model Automatic arabic grammatical error correction based on expectation-maximization routing and target-bidirectional agreement

Reference 9

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This paper cites Optimizing the impact of data augmentation for low-resource grammatical error correction.

Towards the Development of Balanced Synthetic Data for Correcting Grammatical Errors in Arabic: An Approach Based on Error Tagging Model and Synthetic Data Generating Model Optimizing the impact of data augmentation for low-resource grammatical error correction

Reference 10

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This paper cites ChatGPT for Arabic Grammatical Error Correction.

Towards the Development of Balanced Synthetic Data for Correcting Grammatical Errors in Arabic: An Approach Based on Error Tagging Model and Synthetic Data Generating Model ChatGPT for Arabic Grammatical Error Correction

Reference 11

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This paper cites Automatic Error Type Annotation for Arabic.

Towards the Development of Balanced Synthetic Data for Correcting Grammatical Errors in Arabic: An Approach Based on Error Tagging Model and Synthetic Data Generating Model Automatic Error Type Annotation for Arabic

Reference 12

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Towards the Development of Balanced Synthetic Data for Correcting Grammatical Errors in Arabic: An Approach Based on Error Tagging Model and Synthetic Data Generating Model Arabic grammatical error detection using transformers-based pretrained language models

Reference 13

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This paper cites A7’ ta: Data on a monolingual arabic parallel corpus for grammar checking.

Towards the Development of Balanced Synthetic Data for Correcting Grammatical Errors in Arabic: An Approach Based on Error Tagging Model and Synthetic Data Generating Model A7’ ta: Data on a monolingual arabic parallel corpus for grammar checking

Reference 14

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This paper cites Arabert: Transformer-based model for arabic language under- standing.

Towards the Development of Balanced Synthetic Data for Correcting Grammatical Errors in Arabic: An Approach Based on Error Tagging Model and Synthetic Data Generating Model Arabert: Transformer-based model for arabic language under- standing

Reference 15

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This paper cites Leveraging offensive language for sarcasm and sentiment detection in arabic.

Towards the Development of Balanced Synthetic Data for Correcting Grammatical Errors in Arabic: An Approach Based on Error Tagging Model and Synthetic Data Generating Model Leveraging offensive language for sarcasm and sentiment detection in arabic

Reference 16

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This paper cites ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic.

Towards the Development of Balanced Synthetic Data for Correcting Grammatical Errors in Arabic: An Approach Based on Error Tagging Model and Synthetic Data Generating Model ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic

Reference 17

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Towards the Development of Balanced Synthetic Data for Correcting Grammatical Errors in Arabic: An Approach Based on Error Tagging Model and Synthetic Data Generating Model The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models

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Towards the Development of Balanced Synthetic Data for Correcting Grammatical Errors in Arabic: An Approach Based on Error Tagging Model and Synthetic Data Generating Model DeBERTa: Decoding-enhanced BERT with Disentangled Attention

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Towards the Development of Balanced Synthetic Data for Correcting Grammatical Errors in Arabic: An Approach Based on Error Tagging Model and Synthetic Data Generating Model DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing

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This paper cites 1.5 billion words Arabic Corpus.

Towards the Development of Balanced Synthetic Data for Correcting Grammatical Errors in Arabic: An Approach Based on Error Tagging Model and Synthetic Data Generating Model 1.5 billion words Arabic Corpus

Reference 21

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Towards the Development of Balanced Synthetic Data for Correcting Grammatical Errors in Arabic: An Approach Based on Error Tagging Model and Synthetic Data Generating Model Osian: Open source international arabic news corpus-preparation and integration into the clarin-infrastructure

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This paper cites A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages.

Towards the Development of Balanced Synthetic Data for Correcting Grammatical Errors in Arabic: An Approach Based on Error Tagging Model and Synthetic Data Generating Model A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages

Reference 23

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Towards the Development of Balanced Synthetic Data for Correcting Grammatical Errors in Arabic: An Approach Based on Error Tagging Model and Synthetic Data Generating Model Asynchronous pipeline for processing huge corpora on medium to low resource infrastructures

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This paper cites Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books.

Towards the Development of Balanced Synthetic Data for Correcting Grammatical Errors in Arabic: An Approach Based on Error Tagging Model and Synthetic Data Generating Model Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books

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Towards the Development of Balanced Synthetic Data for Correcting Grammatical Errors in Arabic: An Approach Based on Error Tagging Model and Synthetic Data Generating Model Openwebtext corpus, 2019

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Towards the Development of Balanced Synthetic Data for Correcting Grammatical Errors in Arabic: An Approach Based on Error Tagging Model and Synthetic Data Generating Model A Simple Method for Commonsense Reasoning

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Towards the Development of Balanced Synthetic Data for Correcting Grammatical Errors in Arabic: An Approach Based on Error Tagging Model and Synthetic Data Generating Model Exploring the limits of transfer learning with a unified text-to-text transformer

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Towards the Development of Balanced Synthetic Data for Correcting Grammatical Errors in Arabic: An Approach Based on Error Tagging Model and Synthetic Data Generating Model mT5: A massively multilingual pre-trained text-to-text transformer

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Towards the Development of Balanced Synthetic Data for Correcting Grammatical Errors in Arabic: An Approach Based on Error Tagging Model and Synthetic Data Generating Model Byt5: Towards a token-free future with pre-trained byte-to-byte models

Reference 30

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Towards the Development of Balanced Synthetic Data for Correcting Grammatical Errors in Arabic: An Approach Based on Error Tagging Model and Synthetic Data Generating Model AraT5: Text-to-Text Transformers for Arabic Language Generation

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Towards the Development of Balanced Synthetic Data for Correcting Grammatical Errors in Arabic: An Approach Based on Error Tagging Model and Synthetic Data Generating Model Arabic learner corpora (alc): a taxonomy of coding errors

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Towards the Development of Balanced Synthetic Data for Correcting Grammatical Errors in Arabic: An Approach Based on Error Tagging Model and Synthetic Data Generating Model Arabic learners written corpus: A resource for research and learning

Reference 33

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Towards the Development of Balanced Synthetic Data for Correcting Grammatical Errors in Arabic: An Approach Based on Error Tagging Model and Synthetic Data Generating Model Arabic tokenization, part-of-speech tagging and morphological disambiguation in one fell swoop

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Towards the Development of Balanced Synthetic Data for Correcting Grammatical Errors in Arabic: An Approach Based on Error Tagging Model and Synthetic Data Generating Model Mada+ tokan: A toolkit for arabic tokenization, diacritization, morphological disambiguation, pos tagging, stemming and lemmatization

Reference 35

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Towards the Development of Balanced Synthetic Data for Correcting Grammatical Errors in Arabic: An Approach Based on Error Tagging Model and Synthetic Data Generating Model Zaebuc: An annotated arabic-english bilingual writer corpus

Reference 36

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Towards the Development of Balanced Synthetic Data for Correcting Grammatical Errors in Arabic: An Approach Based on Error Tagging Model and Synthetic Data Generating Model Balancing Methods for Multi-label Text Classification with Long-Tailed Class Distribution

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Towards the Development of Balanced Synthetic Data for Correcting Grammatical Errors in Arabic: An Approach Based on Error Tagging Model and Synthetic Data Generating Model A unified view of multi-label performance measures

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Towards the Development of Balanced Synthetic Data for Correcting Grammatical Errors in Arabic: An Approach Based on Error Tagging Model and Synthetic Data Generating Model An evaluation of statistical approaches to text categorization

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Towards the Development of Balanced Synthetic Data for Correcting Grammatical Errors in Arabic: An Approach Based on Error Tagging Model and Synthetic Data Generating Model Multilabel classification

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Towards the Development of Balanced Synthetic Data for Correcting Grammatical Errors in Arabic: An Approach Based on Error Tagging Model and Synthetic Data Generating Model Effective multi-label active learning for text classification

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Towards the Development of Balanced Synthetic Data for Correcting Grammatical Errors in Arabic: An Approach Based on Error Tagging Model and Synthetic Data Generating Model Better evaluation for grammatical error correction

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Towards the Development of Balanced Synthetic Data for Correcting Grammatical Errors in Arabic: An Approach Based on Error Tagging Model and Synthetic Data Generating Model Transformers: State-of-the-art natural language processing

Reference 43

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