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2023 EMNLP EMNLP 2023

Machine Translation of Omani Arabic Dialect from Social Media

Abstract

AbstractResearch studies on Machine Translation (MT) between Modern Standard Arabic (MSA) and English are abundant. However, studies on MT between Omani Arabic (OA) dialects and English are very scarce. This research study focuses on the lack of availability of an Omani dialect parallel dataset, as well as MT of OA to English. The study uses social media data from X (formerly Twitter) to build an authentic parallel text of the Omani dialects. The research presents baseline results on this dataset using Google Translate, Microsoft Translation, and Marian NMT. A taxonomy of the most common linguistic errors is used to analyze the translations made by the NMT systems to provide insights on future improvements. Finally, transfer learning is used to adapt Marian NMT to the Omani dialect, which significantly improved by 9.88 points in the BLEU score.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Deep Learning and Machine Learning and Natural Language Processing
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Khoula Al-Kharusi , Abdurahman AAlAbdulsalam

Topics

Artificial Intelligence > Learning Paradigms > Transfer Learning Natural Language Processing > Applications > Machine Translation Natural Language Processing > Resources & Methods > Multilingual NLP Machine Learning > Learning Types > Transfer Learning Deep Learning > Models > Neural Networks Deep Learning > Learning Types > Transfer Learning

Keywords

transfer learning social media analysis neural machine translation parallel corpus arabic dialect bleu score social media dialectal arabic
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