West African Pidgin English is a language that is significantly spoken in West Africa, consisting of at least 75 million speakers. Nevertheless, proper machine translation systems and relevant NLP datasets for pidgin English are virtually absent. In this work, we develop techniques targeted at bridging the gap between Pidgin English and English in the context of natural language generation. %As a proof of concept, we explore the proposed techniques in the area of data-to-text generation. By building upon the previously released monolingual Pidgin English text and parallel English data-to-text corpus, we hope to build a system that can automatically generate Pidgin English descriptions from structured data. We first train a data-to-English text generation system, before employing techniques in unsupervised neural machine translation and self-training to establish the Pidgin-to-English cross-lingual alignment. The human evaluation performed on the generated Pidgin texts shows that, though still far from being practically usable, the pivoting + self-training technique improves both Pidgin text fluency and relevance.

该研究致力于使用自然语言生成技术，通过建立一个能够根据结构化数据自动生成皮钦英语描述的系统，填补皮钦英语机器翻译系统和相关自然语言处理数据集的空缺。通过训练以英文为目标语的数据文本生成系统，然后采用无监督的神经机器翻译技术和自我训练等方法，我们在Pidgin-to-English跨语言对齐方面取得了进展。结果表明，虽然还远未达到实际使用的水平，但基于中心轴和自我训练技术可以提高皮钦文本的流畅性和相关性。

通过将英语数据作为转换点和自我训练生成未加监督的洋泾浜文本