FArSS: Fast and Efficient Semantic Question Similarity in Arabic
This paper addresses the challenge of efficient semantic question similarity in Arabic by leveraging fastText embeddings and a simple neural network architecture. Our model (FArSS) avoids the complexities of recurrent connections and attention mechanisms, resulting in a streamlined and efficient app...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10840214/ |
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| _version_ | 1832592936127168512 |
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| author | Mohamed Alkaoud |
| author_facet | Mohamed Alkaoud |
| author_sort | Mohamed Alkaoud |
| collection | DOAJ |
| description | This paper addresses the challenge of efficient semantic question similarity in Arabic by leveraging fastText embeddings and a simple neural network architecture. Our model (FArSS) avoids the complexities of recurrent connections and attention mechanisms, resulting in a streamlined and efficient approach. With strategic data augmentation, our model achieves an F1-score of 0.928, closely competing with state-of-the-art models that rely on advanced architectures employing self-attention mechanisms. Additionally, our model outperforms both GPT-4o and GPT-4 in semantic question similarity in Arabic, underscoring the potential of specialized, efficient models to surpass large language models in specific tasks. This work demonstrates that our method not only maintains high performance but also ensures fast training and inference times. The practical advantages of our approach make it especially suitable for real-time applications, contributing to the development of more effective and efficient natural language processing systems. Our findings highlight the continued importance of efficient tailored models in addressing specific natural language processing challenges. |
| format | Article |
| id | doaj-art-81db9a7e9d624f9a86d08f465cef2d68 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-81db9a7e9d624f9a86d08f465cef2d682025-01-21T00:00:55ZengIEEEIEEE Access2169-35362025-01-0113109441095310.1109/ACCESS.2025.352952710840214FArSS: Fast and Efficient Semantic Question Similarity in ArabicMohamed Alkaoud0https://orcid.org/0009-0005-5297-5189Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi ArabiaThis paper addresses the challenge of efficient semantic question similarity in Arabic by leveraging fastText embeddings and a simple neural network architecture. Our model (FArSS) avoids the complexities of recurrent connections and attention mechanisms, resulting in a streamlined and efficient approach. With strategic data augmentation, our model achieves an F1-score of 0.928, closely competing with state-of-the-art models that rely on advanced architectures employing self-attention mechanisms. Additionally, our model outperforms both GPT-4o and GPT-4 in semantic question similarity in Arabic, underscoring the potential of specialized, efficient models to surpass large language models in specific tasks. This work demonstrates that our method not only maintains high performance but also ensures fast training and inference times. The practical advantages of our approach make it especially suitable for real-time applications, contributing to the development of more effective and efficient natural language processing systems. Our findings highlight the continued importance of efficient tailored models in addressing specific natural language processing challenges.https://ieeexplore.ieee.org/document/10840214/Arabic NLPefficient machine learningfastTextmachine learningnatural language processingneural networks |
| spellingShingle | Mohamed Alkaoud FArSS: Fast and Efficient Semantic Question Similarity in Arabic IEEE Access Arabic NLP efficient machine learning fastText machine learning natural language processing neural networks |
| title | FArSS: Fast and Efficient Semantic Question Similarity in Arabic |
| title_full | FArSS: Fast and Efficient Semantic Question Similarity in Arabic |
| title_fullStr | FArSS: Fast and Efficient Semantic Question Similarity in Arabic |
| title_full_unstemmed | FArSS: Fast and Efficient Semantic Question Similarity in Arabic |
| title_short | FArSS: Fast and Efficient Semantic Question Similarity in Arabic |
| title_sort | farss fast and efficient semantic question similarity in arabic |
| topic | Arabic NLP efficient machine learning fastText machine learning natural language processing neural networks |
| url | https://ieeexplore.ieee.org/document/10840214/ |
| work_keys_str_mv | AT mohamedalkaoud farssfastandefficientsemanticquestionsimilarityinarabic |