Simplification of Arabic text: A hybrid approach integrating machine translation and transformer-based lexical model
The process of text simplification (TS) is crucial for enhancing the comprehension of written material, especially for people with low literacy levels and those who struggle to understand written content. In this study, we introduce the first automated approach to TS that combines word-level and sen...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-09-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157823002161 |
| _version_ | 1797663912914583552 |
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| author | Suha S. Al-Thanyyan Aqil M. Azmi |
| author_facet | Suha S. Al-Thanyyan Aqil M. Azmi |
| author_sort | Suha S. Al-Thanyyan |
| collection | DOAJ |
| description | The process of text simplification (TS) is crucial for enhancing the comprehension of written material, especially for people with low literacy levels and those who struggle to understand written content. In this study, we introduce the first automated approach to TS that combines word-level and sentence-level simplification techniques for Arabic text. We employ three models: a neural machine translation model, an Arabic-BERT-based lexical model, and a hybrid model that combines both methods to simplify the text. To evaluate the models, we created and utilized two Arabic datasets, namely EW-SEW and WikiLarge, comprising 82,585 and 249 sentence pairs, respectively. As resources were scarce, we made these datasets available to other researchers. The EW-SEW dataset is a commonly used English TS corpus that aligns each sentence in the original English Wikipedia (EW) with a simpler reference sentence from Simple English Wikipedia (SEW). In contrast, the WikiLarge dataset has eight simplified reference sentences for each of the 249 test sentences. The hybrid model outperformed the other models, achieving a BLEU score of 55.68, a SARI score of 37.15, and an FBERT score of 86.7% on the WikiLarge dataset, demonstrating the effectiveness of the combined approach. |
| first_indexed | 2024-03-11T19:21:40Z |
| format | Article |
| id | doaj.art-fc8247cd3d9e4be1bc5e6ad24ca39bc3 |
| institution | Directory Open Access Journal |
| issn | 1319-1578 |
| language | English |
| last_indexed | 2024-03-11T19:21:40Z |
| publishDate | 2023-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj.art-fc8247cd3d9e4be1bc5e6ad24ca39bc32023-10-07T04:34:00ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782023-09-01358101662Simplification of Arabic text: A hybrid approach integrating machine translation and transformer-based lexical modelSuha S. Al-Thanyyan0Aqil M. Azmi1Department of Computer Science, College of Computer & Information Sciences, King Saud University, Riyadh 11543, Saudi ArabiaCorresponding author.; Department of Computer Science, College of Computer & Information Sciences, King Saud University, Riyadh 11543, Saudi ArabiaThe process of text simplification (TS) is crucial for enhancing the comprehension of written material, especially for people with low literacy levels and those who struggle to understand written content. In this study, we introduce the first automated approach to TS that combines word-level and sentence-level simplification techniques for Arabic text. We employ three models: a neural machine translation model, an Arabic-BERT-based lexical model, and a hybrid model that combines both methods to simplify the text. To evaluate the models, we created and utilized two Arabic datasets, namely EW-SEW and WikiLarge, comprising 82,585 and 249 sentence pairs, respectively. As resources were scarce, we made these datasets available to other researchers. The EW-SEW dataset is a commonly used English TS corpus that aligns each sentence in the original English Wikipedia (EW) with a simpler reference sentence from Simple English Wikipedia (SEW). In contrast, the WikiLarge dataset has eight simplified reference sentences for each of the 249 test sentences. The hybrid model outperformed the other models, achieving a BLEU score of 55.68, a SARI score of 37.15, and an FBERT score of 86.7% on the WikiLarge dataset, demonstrating the effectiveness of the combined approach.http://www.sciencedirect.com/science/article/pii/S1319157823002161Text simplificationArabic text simplificationLexical simplificationNeural machine translationTransformersArabic corpora |
| spellingShingle | Suha S. Al-Thanyyan Aqil M. Azmi Simplification of Arabic text: A hybrid approach integrating machine translation and transformer-based lexical model Journal of King Saud University: Computer and Information Sciences Text simplification Arabic text simplification Lexical simplification Neural machine translation Transformers Arabic corpora |
| title | Simplification of Arabic text: A hybrid approach integrating machine translation and transformer-based lexical model |
| title_full | Simplification of Arabic text: A hybrid approach integrating machine translation and transformer-based lexical model |
| title_fullStr | Simplification of Arabic text: A hybrid approach integrating machine translation and transformer-based lexical model |
| title_full_unstemmed | Simplification of Arabic text: A hybrid approach integrating machine translation and transformer-based lexical model |
| title_short | Simplification of Arabic text: A hybrid approach integrating machine translation and transformer-based lexical model |
| title_sort | simplification of arabic text a hybrid approach integrating machine translation and transformer based lexical model |
| topic | Text simplification Arabic text simplification Lexical simplification Neural machine translation Transformers Arabic corpora |
| url | http://www.sciencedirect.com/science/article/pii/S1319157823002161 |
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