摘要
针对航海领域中船舶自动识别系统(AIS)无法利用中文给我国使用者带来的识别障碍问题,研究了AIS拼音信息到汉字的智能翻译技术.在建立标准化汉字和拼音船名语料库的基础上,分别搭建了基于Seq2Seq和Transformer框架的智能船名翻译的深度学习模型.通过在同一数据集上的性能对比分析发现,Transformer模型具有更好的效果.为弥补Transformer模型受语料库规模限制而带来的翻译损失,进一步研究了其与隐马尔科夫链(HMM)的联合翻译模型,最终,在测试集上达到了98.92%的准确率,实现了对AIS拼音船名的精准匹配和合理翻译.该模型同样适用于AIS中目的港等拼音信息到汉字的翻译,对于提升AIS信息使用者的体验具有实际应用价值.
In order to solve the problem that Automatic Identification System(AIS) can’t make use of Chinese in the field of navigation,which has brought identification problems to Chinese users, this paper studies the intelligent translation technology from AIS Pinyin information to Chinese characters. Based on the establishment of standardized Chinese characters and Pinyin ship name corpus, deep learning models of intelligent ship names translation based on Seq2 Seq and Transformer frameworks were built respectively. Through the performance comparison analysis on the same dataset, it is found that the Transformer model has better effects. In order to compensate for the translation loss caused by the limitation of the corpus size of the Transformer model, the combined translation model of the Transformer model and the Hidden Markov Chain(HMM) was further studied. Finally, the accuracy of the test set is 98.92%, and the accurate matching and reasonable translation of the AIS Pinyin ship names are realized. The model is also applicable to the translation of other Pinyin information into Chinese characters in AIS, such as destination port. It has application value for improving the experience of AIS information users.
作者
潘明阳
李琦
盛尊阔
韩斌
李超
李邵喜
PAN Ming-yang;LI Qi;SHENG Zun-kuo;HAN Bin;LI Chao;LI Shao-xi(Navigation College,Dalian Maritime University,Dalian 116026,C hina;Elane Inc.,Beijing 100089,China)
出处
《大连海事大学学报》
CAS
CSCD
北大核心
2020年第2期41-48,共8页
Journal of Dalian Maritime University
基金
国家自然科学基金资助项目(51579025)
中央高校基本科研业务费专项资金资助项目(3132019400)。
