Machine translation has made significant progress in recent years with advancements in Natural Language Processing (NLP) technology. This paper introduces a novel Seq2Seq model that aims to improve translation quality while reducing the storage space required by the model.
The proposed model utilizes a Bidirectional Long Short-Term Memory network (Bi-LSTM) as the encoder, which allows it to capture the context information of the input sequence effectively. This is an important aspect in ensuring accurate and high-quality translations. Additionally, the decoder incorporates an attention mechanism, which further enhances the model’s ability to focus on key information during the translation process. This attention mechanism is particularly useful in handling long or complex sentences.
One notable advantage of this model is its size. Compared to the current mainstream Transformer model, the proposed model achieves superior performance, while maintaining a smaller size. This is a critical factor in real-world applications, as smaller models require less computational resources and are more suitable for deployment in resource-constrained scenarios.
To validate the effectiveness of the model, a series of experiments were conducted. These experiments included assessing the model’s performance on different language pairs and comparing it with traditional Seq2Seq models. The results demonstrated that the proposed model not only maintained translation accuracy but also significantly reduced the storage requirements.
The reduction in storage requirements is of great significance, as it enables the model to be deployed on devices with limited memory capacity or in situations where internet connectivity is limited. This makes the model practical and versatile, opening up opportunities for translation applications in various resource-constrained scenarios.
In summary, this paper presents a novel Seq2Seq model that combines a Bi-LSTM encoder with an attention mechanism in the decoder. The model achieves superior performance on the WMT14 machine translation dataset while maintaining a smaller size compared to the mainstream Transformer model. The reduction in storage requirements is a significant advantage, making the model suitable for resource-constrained scenarios. Overall, this research contributes to the advancement of machine translation technology and has practical implications for real-world application.
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