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Arabic paraphrased parallel synthetic dataset
Noora Al-Shameri1, Hend Al-Khalifa1
1Information Technology Department, King Saud University, Riyadh, Saudi Arabia.
Data in Brief
|November 5, 2024
Summary
This study introduces a new Arabic paraphrased parallel dataset created using back-translation. This resource aims to improve natural language processing (NLP) tasks and Arabic language understanding.
Area of Science:
- Natural Language Processing (NLP)
- Computational Linguistics
- Data Science
Background:
- Natural Language Processing (NLP) applications require robust datasets for Arabic.
- Existing Arabic datasets lack sufficient paraphrased parallel data.
- Data augmentation is crucial for expanding linguistic resources.
Purpose of the Study:
- To develop a comprehensive Arabic paraphrased parallel dataset.
- To address the scarcity of paraphrase generation resources for Arabic.
- To enhance various NLP tasks through improved data.
Main Methods:
- Sentence collection from diverse Arabic sources.
- Data augmentation using the back-translation technique for paraphrasing.
- Preprocessing and rigorous evaluation of the generated dataset.
Main Results:
- A reliable and useful Arabic paraphrased parallel dataset was created.
- The dataset effectively addresses the gap in Arabic paraphrase generation research.
- The dataset is suitable for diverse NLP applications.
Conclusions:
- The developed dataset is vital for advancing Arabic NLP.
- It supports machine translation, text summarization, and sentiment analysis.
- This resource fosters innovation in Arabic language processing and related fields.
Keywords:
Data augmentationMachine translationModern standard ArabicNatural language processingParaphrase generationMore Related Videos
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