An Enhanced Text Compression Approach Using Transformer-based Language Models
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866917875333201920 |
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| author | Rahman, Chowdhury Mofizur Sobhani, Mahbub E Rodela, Anika Tasnim Shatabda, Swakkhar |
| author_facet | Rahman, Chowdhury Mofizur Sobhani, Mahbub E Rodela, Anika Tasnim Shatabda, Swakkhar |
| contents | Text compression shrinks textual data while keeping crucial information, eradicating constraints on storage, bandwidth, and computational efficacy. The integration of lossless compression techniques with transformer-based text decompression has received negligible attention, despite the increasing volume of English text data in communication. The primary barrier in advancing text compression and restoration involves optimizing transformer-based approaches with efficient pre-processing and integrating lossless compression algorithms, that remained unresolved in the prior attempts. Here, we propose a transformer-based method named RejuvenateForme for text decompression, addressing prior issues by harnessing a new pre-processing technique and a lossless compression method. Our meticulous pre-processing technique incorporating the Lempel-Ziv-Welch algorithm achieves compression ratios of 12.57, 13.38, and 11.42 on the BookCorpus, EN-DE, and EN-FR corpora, thus showing state-of-the-art compression ratios compared to other deep learning and traditional approaches. Furthermore, the RejuvenateForme achieves a BLEU score of 27.31, 25.78, and 50.45 on the EN-DE, EN-FR, and BookCorpus corpora, showcasing its comprehensive efficacy. In contrast, the pre-trained T5-Small exhibits better performance over prior state-of-the-art models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_15250 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An Enhanced Text Compression Approach Using Transformer-based Language Models Rahman, Chowdhury Mofizur Sobhani, Mahbub E Rodela, Anika Tasnim Shatabda, Swakkhar Computation and Language Information Theory Machine Learning Text compression shrinks textual data while keeping crucial information, eradicating constraints on storage, bandwidth, and computational efficacy. The integration of lossless compression techniques with transformer-based text decompression has received negligible attention, despite the increasing volume of English text data in communication. The primary barrier in advancing text compression and restoration involves optimizing transformer-based approaches with efficient pre-processing and integrating lossless compression algorithms, that remained unresolved in the prior attempts. Here, we propose a transformer-based method named RejuvenateForme for text decompression, addressing prior issues by harnessing a new pre-processing technique and a lossless compression method. Our meticulous pre-processing technique incorporating the Lempel-Ziv-Welch algorithm achieves compression ratios of 12.57, 13.38, and 11.42 on the BookCorpus, EN-DE, and EN-FR corpora, thus showing state-of-the-art compression ratios compared to other deep learning and traditional approaches. Furthermore, the RejuvenateForme achieves a BLEU score of 27.31, 25.78, and 50.45 on the EN-DE, EN-FR, and BookCorpus corpora, showcasing its comprehensive efficacy. In contrast, the pre-trained T5-Small exhibits better performance over prior state-of-the-art models. |
| title | An Enhanced Text Compression Approach Using Transformer-based Language Models |
| topic | Computation and Language Information Theory Machine Learning |
| url | https://arxiv.org/abs/2412.15250 |