基于深度学习的自然语言处理技术在智能翻译系统中的应用研究

doi:10.11896/jsjkx.241000037

摘要/Abstract

摘要： 随着全球化进程的加速,翻译需求日益增长,智能翻译系统的重要性愈发凸显。文中深入研究基于深度学习的自然语言处理技术在智能翻译系统中的应用。首先基于深度学习的智能翻译系统主要依托循环神经网络、长短期记忆网络和卷积神经网络等架构,通过词向量表示和语义理解技术实现高质量翻译。在系统架构方面,编码器-解码器框架结合注意力机制显著提升了翻译质量,而基于Transformer的模型则在处理长距离依赖关系方面取得突破性进展。在实践应用中,谷歌神经机器翻译系统和CUBBITT等系统通过创新的数据增强技术和多语言模型训练方法,实现了接近人类水平的翻译效果。然而,当前智能翻译系统在处理语义歧义、适应语言多样性和跨文化理解等方面仍面临重大挑战。针对这些问题,提出了多源信息融合、跨语言预训练和知识增强等解决方案,并在准确度、流畅度等评价指标上取得显著进展。未来智能翻译系统的发展将朝着多模态融合、知识驱动和轻量化部署等方向发展,同时也需要进一步提升在低资源语言翻译和模型可解释性等方面的能力。

关键词: 深度学习, 自然语言处理, 神经网络机器翻译, 智能翻译系统, 技术挑战

Abstract: With the acceleration of globalization,the demand for translation is increasing,and the importance of intelligent translation systems is becoming increasingly prominent.This paper deeply studies the application of natural language processing technology based on deep learning in intelligent translation systems.Firstly,the intelligent translation system based on deep learning mainly relies on the architecture of recurrent neural networks,long short-term memory networks,and convolutional neural networks,and achieves high-quality translation through word vector representation and semantic understanding technology.In terms of system architecture,the encoder-decoder framework combined with attention mechanism significantly improves the quality of translation,while the Transformer-based model has made breakthroughs in handling long-distance dependencies.In practical applications,systems such as Google Neural Machine Translation and CUBBITT have achieved near-human translation performance through innovative data enhancement techniques and multilingual model training methods.However,current intelligent translation systems still face significant challenges in dealing with semantic ambiguity,adapting to linguistic diversity,and cross-cultural understanding.To address these issues,researchers have proposed solutions such as multi-source information fusion,cross-language pre-training,and knowledge enhancement,and have made significant progress in evaluation metrics such as accuracy and fluency.The future development of intelligent translation systems will be towards multi-modal fusion,knowledge-driven and lightweight deployment,while also needing to further improve capabilities in low-resource language translation and model interpretability.

Key words: Deep learning, Natural language processing, Neural network machine translation, Intelligent translation systems, Technical challenges

中图分类号:

TP391

傅娟. 基于深度学习的自然语言处理技术在智能翻译系统中的应用研究[J]. 计算机科学, 2025, 52(11A): 241000037-6. https://doi.org/10.11896/jsjkx.241000037

FU Juan. Research on Application of Deep Learning-based Natural Language Processing Technology inIntelligent Translation Systems[J]. Computer Science, 2025, 52(11A): 241000037-6. https://doi.org/10.11896/jsjkx.241000037

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