我們提出了新穎的方法，用于評估資源貧乏的語言的機器翻譯模型的人工翻譯對齊文本的品質。馬裡的大學生翻譯了法國文本，并向班巴拉提供了書面或口頭翻譯。我們的結果表明，從某些類型的文字的書面或口頭翻譯中可以獲得類似的品質。他們還建議應提供人工翻譯以提高工作品質的具體說明。
原文标題：Assessing Human Translations from French to Bambara for Machine Learning: a Pilot Study
原文：We present novel methods for assessing the quality of human-translated aligned texts for learning machine translation models of under-resourced languages. Malian university students translated French texts, producing either written or oral translations to Bambara. Our results suggest that similar quality can be obtained from either written or spoken translations for certain kinds of texts. They also suggest specific instructions that human translators should be given in order to improve the quality of their work.
原文作者：Michael Leventhal, Allahsera Tapo, Sarah Luger, Marcos Zampieri, Christopher M. Homan
原文位址：https://arxiv.org/abs/2004.00068
評估從法語到班巴拉語的人工翻譯，以進行機器學習：一項初步研究(CS.CL).pdf