Creating a Corpus for Russian Data-to-Text Generation Using Neural Machine Translation and Post-Editing

被引：0

作者：

Shimorina, Anastasia ^{[1
,2
]}

Khasanova, Elena ^{[1
]}

Gardent, Claire ^{[2
,3
]}

机构：

[1] Lorraine Univ, Nancy, France

[2] LORIA, Nancy, France

[3] CNRS, Nancy, France

来源：

7TH WORKSHOP ON BALTO-SLAVIC NATURAL LANGUAGE PROCESSING (BSNLP'2019) | 2019年

关键词：

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：

In this paper, we propose an approach for semi-automatically creating a data-to-text (D2T) corpus for Russian that can be used to learn a D2T natural language generation model. An error analysis of the output of an English-to-Russian neural machine translation system shows that 80% of the automatically translated sentences contain an error and that 53% of all translation errors bear on named entities (NE). We therefore focus on named entities and introduce two post-editing techniques for correcting wrongly translated NEs.

引用

页码：44 / 49

页数：6

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