基于人工智能的古文翻译新框架

IF 1.7 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Shikha Verma, Neha Gupta, Anil B C, Rosey Chauhan
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引用次数: 0

摘要

古代文字一直是知识、文化和文明史的宝库。为了更好地获取古代文字中存在的有价值的信息,需要开发一个适当的翻译系统,同时考虑到复杂性和对文字的了解非常少。本研究采用人工智能技术实现了一个翻译预测系统。使用sunda数据集和自生成数据集进行训练,而从古代文字(即Sundanese文字)到英语文本的翻译则使用两层递归神经网络完成。所使用的技术与现有的称为IM translator的翻译器进行了比较。结果表明,BLEU分数比IM Translator提高了8%,而WER分数比IM Translator降低了10%。此外,N-Gram分析结果表明100%对比度值增加3%至4%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Novel Framework for Ancient Text Translation Using Artificial Intelligence
Ancient script has been a repository of knowledge, culture and civilization history. In order to have a greater access to the valuable information present in the ancient scripts, an appropriate translation system needs to be developed keeping complexity and very less knowledge of the script available in consideration. In this study, a translation and prediction system has been implemented using Artificial Intelligence. The training has been developed using Sunda-Dataset and self-generated dataset, whereas the translation from ancient script viz. Sundanese script to English text is done using two layers Recurrent Neural Network. The technique used is compared with an existing translator called IM Translator. The results shows that the BLEU score  is increased by 8% in comparison to IM Translator further WER is decreased  by 10% in contrast to IM Translator.  Furthermore, the N-Gram analysis results indicate 3% to 4% increase in 100% contrast value. 
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来源期刊
CiteScore
1.40
自引率
0.00%
发文量
22
审稿时长
4 weeks
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