# Data Augmentation using Transformers and Similarity Measures for Improving Arabic Text Classification

arXiv: https://arxiv.org/abs/2212.13939 · HTML: https://mlsanity.com/paper/2212.13939/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 62 references that are in the map.
- First posted on arXiv 28 Dec 2022.

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks](https://mlsanity.com/paper/1901.11196/) (2019)
- [A Survey of Data Augmentation Approaches for NLP](https://mlsanity.com/paper/2105.03075/) (2021)
- [A Survey of Transformers](https://mlsanity.com/paper/2106.04554/) (2021)
- [AraBERT: Transformer-based Model for Arabic Language Understanding](https://mlsanity.com/paper/2003.00104/) (2020)
- [Evaluation of Text Generation: A Survey](https://mlsanity.com/paper/2006.14799/) (2020)
- [ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic](https://mlsanity.com/paper/2101.01785/) (2020)
- [Text Classification Algorithms: A Survey](https://mlsanity.com/paper/1904.08067/) (2019)
- [Deep Learning for Sentiment Analysis : A Survey](https://mlsanity.com/paper/1801.07883/) (2018)
- [An Empirical Survey of Data Augmentation for Limited Data Learning in NLP](https://mlsanity.com/paper/2106.07499/) (2021)
- [Data Augmentation Approaches in Natural Language Processing: A Survey](https://mlsanity.com/paper/2110.01852/) (2021)
- [An Analysis of Simple Data Augmentation for Named Entity Recognition](https://mlsanity.com/paper/2010.11683/) (2020)
- [Sequence-to-Sequence Data Augmentation for Dialogue Language Understanding](https://mlsanity.com/paper/1807.01554/) (2018)
- [AraGPT2: Pre-Trained Transformer for Arabic Language Generation](https://mlsanity.com/paper/2012.15520/) (2020)
- [Improving Zero and Few-Shot Abstractive Summarization with Intermediate Fine-tuning and Data Augmentation](https://mlsanity.com/paper/2010.12836/) (2020)
- [Word Embeddings: A Survey](https://mlsanity.com/paper/1901.09069/) (2019)
- [AraELECTRA: Pre-Training Text Discriminators for Arabic Language Understanding](https://mlsanity.com/paper/2012.15516/) (2020)
- [Conditional Augmentation for Aspect Term Extraction via Masked Sequence-to-Sequence Generation](https://mlsanity.com/paper/2004.14769/) (2020)
- [Overview of the WANLP 2022 Shared Task on Propaganda Detection in Arabic](https://mlsanity.com/paper/2211.10057/) (2022)
- [C2C-GenDA: Cluster-to-Cluster Generation for Data Augmentation of Slot Filling](https://mlsanity.com/paper/2012.07004/) (2020)
- [BET: A Backtranslation Approach for Easy Data Augmentation in Transformer-based Paraphrase Identification Context](https://mlsanity.com/paper/2009.12452/) (2020)
- [Data Augmentation for Hypernymy Detection](https://mlsanity.com/paper/2005.01854/) (2020)
- [Accenture at CheckThat! 2021: Interesting claim identification and ranking with contextually sensitive lexical training data augmentation](https://mlsanity.com/paper/2107.05684/) (2021)
- [Can We Achieve More with Less? Exploring Data Augmentation for Toxic Comment Classification](https://mlsanity.com/paper/2007.00875/) (2020)
- [BERT Transformer model for Detecting Arabic GPT2 Auto-Generated Tweets](https://mlsanity.com/paper/2101.09345/) (2021)

## Built on by (most cited first, 3 of 3)

- [Enhancing Arabic Automated Essay Scoring with Synthetic Data and Error Injection](https://mlsanity.com/paper/2503.17739/) (2025)
- [A comprehensive survey of contemporary Arabic sentiment analysis: Methods, Challenges, and Future Directions](https://mlsanity.com/paper/2502.03827/) (2025)
- [Towards Inclusive NLP: Assessing Compressed Multilingual Transformers across Diverse Language Benchmarks](https://mlsanity.com/paper/2507.19699/) (2025)

## Abstract (arXiv)

The performance of learning models heavily relies on the availability and adequacy of training data. To address the dataset adequacy issue, researchers have extensively explored data augmentation (DA) as a promising approach. DA generates new data instances through transformations applied to the available data, thereby increasing dataset size and variability. This approach has enhanced model performance and accuracy, particularly in addressing class imbalance problems in classification tasks. However, few studies have explored DA for the Arabic language, relying on traditional approaches such as paraphrasing or noising-based techniques. In this paper, we propose a new Arabic DA method that employs the recent powerful modeling technique, namely the AraGPT-2, for the augmentation process. The generated sentences are evaluated in terms of context, semantics, diversity, and novelty using the Euclidean, cosine, Jaccard, and BLEU distances. Finally, the AraBERT transformer is used on sentiment classification tasks to evaluate the classification performance of the augmented Arabic dataset. The experiments were conducted on four sentiment Arabic datasets: AraSarcasm, ASTD, ATT, and MOVIE. The selected datasets vary in size, label number, and unbalanced classes. The results show that the proposed methodology enhanced the Arabic sentiment text classification on all datasets with an increase in F1 score by 4% in AraSarcasm, 6% in ASTD, 9% in ATT, and 13% in MOVIE.