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Proceedings of the 2024 Ninth International Conference on Research in Intelligent Computing in Engineering

Annals of Computer Science and Information Systems, Volume 42

Seq2Seq Transformer-Based Model for Optimized Chinese-to-English Translation

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DOI: http://dx.doi.org/10.15439/2024R81

Citation: Proceedings of the 2024 Ninth International Conference on Research in Intelligent Computing in Engineering, Vijender Kumar Solanki, Tran Duc Tan, Pradeep Kumar, Manuel Cardona (eds). ACSIS, Vol. 42, pages 1–10 ()

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Abstract. The use of transformer models for machine translation from Chinese to English is examined in this research. The transformer design, which is well-known for its self-attention mechanism, makes it possible to handle Chinese's intricate linguistic structures with efficiency. We assess the model's effectiveness using benchmark datasets, examine its translation correctness through cosine similarity scores, Rouge metric scores and draw attention to important issues including managing context and sentence structure inconsistencies. We also explore situations in which language complexity is observed to result in low accuracy, providing valuable information for enhancing future models. This paper highlights areas for optimization in practical situations and shows how transformers might improve translation quality.

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