Using Dictionary and Lemmatizer to Improve Low Resource English-Malay Statistical Machine Translation System

被引：4

作者：

Yeong, Yin-Lai ^{[1
]}

Tan, Tien-Ping ^{[1
]}

Mohammad, Siti Khaotijah ^{[1
]}

机构：

[1] Univ Sci Malaysia, Sch Comp Sci, Gelugor 11800, Penang, Malaysia

来源：

SLTU-2016 5TH WORKSHOP ON SPOKEN LANGUAGE TECHNOLOGIES FOR UNDER-RESOURCED LANGUAGES | 2016年 / 81卷

关键词：

Statistical machine tranlstion; English-Malay; dictionary; parallel corpus; lemmatization;

D O I：

10.1016/j.procs.2016.04.056

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Statistical Machine Translation (SMT) is one of the most popular methods for machine translation. In this work, we carried out English-Malay SMT by acquiring an English-Malay parallel corpus in computer science domain. On the other hand, the training parallel corpus is from a general domain. Thus, there will be a lot of out of vocabulary during translation. We attempt to improve the English-Malay SMT in computer science domain using a dictionary and an English lemmatizer. Our study shows that a combination of approach using bilingual dictionary and English lemmatization improves the BLEU score for English to Malay translation from 12.90 to 15.41. (C) 2016 The Authors. Published by Elsevier B.V.

引用

页码：243 / 249

页数：7

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