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Abstract <p>This paper briefly described the end-to-end machine translation algorithm based on long short-term memory (LSTM), and introduced part-of-speech auxiliary information into machine translation. The translation algorithm performance under different hidden layer numbers and node numbers and the improved machine translation algorithm designed in this article were tested in simulation experiments. Part-of-speech auxiliary information ablation experiments were conducted. It was found that the translation algorithm achieved optimal performance when there were two hidden layers in the LSTM of the encoder and decoder and 256 hidden layer nodes in each layer. The proposed algorithm had the highest bilingual evaluation understudy (BLEU) score, followed by the LSTM-based algorithm, and the recurrent neural network-based algorithm had the lowest score. The performance of the proposed machine translation algorithm decreased after excluding any part-of-speech, and the exclusion of the verb had the largest impact.</p>

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Accurate Translation of English—Based on LSTM Algorithm and Part-of-Speech Auxiliary

  • 期刊论文

  • 发表日期：

    2025年8月1日

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  • Xiangyu Guo

摘要

Abstract

This paper briefly described the end-to-end machine translation algorithm based on long short-term memory (LSTM), and introduced part-of-speech auxiliary information into machine translation. The translation algorithm performance under different hidden layer numbers and node numbers and the improved machine translation algorithm designed in this article were tested in simulation experiments. Part-of-speech auxiliary information ablation experiments were conducted. It was found that the translation algorithm achieved optimal performance when there were two hidden layers in the LSTM of the encoder and decoder and 256 hidden layer nodes in each layer. The proposed algorithm had the highest bilingual evaluation understudy (BLEU) score, followed by the LSTM-based algorithm, and the recurrent neural network-based algorithm had the lowest score. The performance of the proposed machine translation algorithm decreased after excluding any part-of-speech, and the exclusion of the verb had the largest impact.