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优化交互式神经机器翻译模型在外语童书中的应用

Application of Optimizing Interactive Neural Machine Translation Model in Foreign Language Children’s Books
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摘要 在外语童书的智能翻译领域,设计了一种优化交互式神经机器翻译模型在外语童书中的应用,其中包括汉语与德语之间的童书翻译。该系统在解码阶段提供不同比例的正确译文来实现模型的训练,以应对不同的训练集规模。实验结果表明,在不同规模的训练集下,模型的评估结果随着正确输入比例的增加而持续提升。在1兆字节训练集条件下,分别增加15%到45%的正确输入能够使结果平均提升16.07%。当2兆字节训练集时,同样比例的正确输入增加将使测试结果平均提升了15.84%。训练集为3兆字节,测试结果平均提升量15.65%。因此,训练数据规模对翻译结果和模型表现均有着显著影响,提高了机器翻译的准确性和流畅性,同时为汉德童书翻译提供了新的依据和视角。 In the field of intelligent translation of foreign language children’s books,an optimized interactive neural machine translation model has been designed for application in foreign language children’s books,including the translation of children’s books between Chinese and German.The system provides different proportions of correct translations during the decoding stage to train the model,in order to cope with different training set sizes.The experimental results show that under different scale training sets,the evaluation results of the model continue to improve as the proportion of correct inputs increases.Under the condition of a 1 megabyte training set,adding 15%to 45%of correct inputs can result in an average improvement of 16.07%in the results.When training a set of 2 megabytes,an increase in correct input of the same proportion will result in an average improvement of 15.84%in test results.The training set is 3 megabytes,and the average improvement in test results is 15.65%.Therefore,the scale of training data has a significant impact on translation results and model performance,improving the accuracy and fluency of machine translation,and providing a new basis and perspective for the translation of Chinese German children’s books.
作者 卞红联 BIAN Honglian(Xi’an Fanyi University,Xi’an 710105,China)
机构地区 西安翻译学院
出处 《自动化与仪器仪表》 2024年第6期164-168,共5页 Automation & Instrumentation
基金 2023年度陕西省哲学社会科学研究专项青年项目《传统文化主题类童书对外输出策略研究》(2023QN0384)。
关键词 交互式机器翻译 童书翻译 多头注意力机制 LSTM网络 解码器 interactive machine translation translation of children’s books attention mechanism LSTM network decoder
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