Statistical Machine Translation Pada Bahasa Lampung Dialek Api Ke Bahasa Indonesia

Author:

Permata Permata,Abidin Zaenal

Abstract

In this research, automatic translation of the Lampung dialect into Indonesian was carried out using the statistical machine translation (SMT) approach. Translation of the Lampung language to Indonesian can be done by using a dictionary. Another alternative is to use the Lampung parallel body corpus and its translation in Indonesian with the SMT approach. The SMT approach is carried out in several phases. Starting from the pre-processing phase which is the initial stage to prepare a parallel corpus. Then proceed with the training phase, namely the parallel corpus processing phase to obtain a language model and translation model. Then the testing phase, and ends with the evaluation phase. SMT testing uses 25 single sentences without out-of-vocabulary (OOV), 25 single sentences with OOV, 25 compound sentences without OOV and 25 compound sentences with OOV. The results of testing the translation of Lampung sentences into Indonesian shows the accuracy of the Bilingual Evaluation Undestudy (BLEU) obtained is 77.07% in 25 single sentences without out-of-vocabulary (OOV), 72.29% in 25 single sentences with OOV, 79.84% at 25 compound sentences without OOV and 80.84% at 25 compound sentences with OOV.

Publisher

STMIK Budi Darma

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Translation of the Lampung Language Text Dialect of Nyo into the Indonesian Language with DMT and SMT Approach;INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi;2021-02-01

2. Effect of mono corpus quantity on statistical machine translation Indonesian – Lampung dialect of nyo;Journal of Physics: Conference Series;2021-01-01

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3