Acknowledged as one of the most successful online cooperative projects in
human society, Wikipedia has obtained rapid growth in recent years and desires
continuously to expand content and disseminate knowledge values for everyone
globally. The shortage of volunteers brings to Wikipedia many issues, including
developing content for over 300 languages at the present. Therefore, the
benefit that machines can automatically generate content to reduce human
efforts on Wikipedia language projects could be considerable. In this paper, we
propose our mapping process for the task of converting Wikidata statements to
natural language text (WS2T) for Wikipedia projects at the sentence level. The
main step is to organize statements, represented as a group of quadruples and
triples, and then to map them to corresponding sentences in English Wikipedia.
We evaluate the output corpus in various aspects: sentence structure analysis,
noise filtering, and relationships between sentence components based on word
embedding models. The results are helpful not only for the data-to-text
generation task but also for other relevant works in the field.

本文介绍了一种将 Wikidata 语句转换为英文维基百科文章的任务转化为自然语言文本的方法，并且对输出内容进行了句子结构分析，噪声过滤以及基于词嵌入模型的句子构成关系评估。