Statistical Machine Translation

被引：0

作者：

Babhulgaonkar, A. R. ^{[1
]}

Bharad, S. V. ^{[1
]}

机构：

[1] Dr BA Tech Uni, Dept Info Tech, Raigad, Maharashtra, India

来源：

2017 1ST INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS AND INFORMATION MANAGEMENT (ICISIM) | 2017年

关键词：

Machine Translation (MT); Phrase Based Statistical Machine Translation (PBSMT); language modeling; word alignment;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Translation of natural language text using statistical machine translation (SMT) is a supervised machine learning problem. SMT algorithms are trained to learn how to translate by providing many translations produced by human language experts. The field SMT has gained momentum in recent three decades. New techniques are constantly introduced by the researchers. This is survey paper presenting an introduction of the recent developments in the field. The paper also describes the recent research for word alignment and language modelling problems in the translation process. An overview of these two sub problems is enlisted. Along the way, some challenges in machine translation are presented.

引用

页码：62 / 67

页数：6