An Experimental Study on Improved Sequence-to-Sequence Model in Machine Translation
This paper presents the N-Seq2Seq model for enhancing machine translation quality and efficiency. The core innovations include streamlined attention mechanisms for focusing on crucial details, word-level tokenization to preserve meaning, text candidate frames for prediction acceleration, and relativ...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
2025-01-01
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| Series: | Tehnički Vjesnik |
| Subjects: | |
| Online Access: | https://hrcak.srce.hr/file/478020 |
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| Summary: | This paper presents the N-Seq2Seq model for enhancing machine translation quality and efficiency. The core innovations include streamlined attention mechanisms for focusing on crucial details, word-level tokenization to preserve meaning, text candidate frames for prediction acceleration, and relative positional encoding reinforcing word associations. Comparative analyses on English-Chinese datasets demonstrate approximately 4 BLEU score improvements over baseline Seq2Seq and 2 BLEU gains over Transformer models. Moreover, the N-Seq2Seq model reduces average inference time by 60% and 43% respectively. These techniques improve contextual modeling, reduce non-essential information, and accelerate reasoning. Importantly, the model achieves higher accuracy with low overhead, making it possible to deploy on mobile applications, while the Chinese-centric design can also be quickly adapted to other languages. |
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| ISSN: | 1330-3651 1848-6339 |