MLQE-PE: A Multilingual Quality Estimation and Post-Editing Dataset
Instituto de Telecomunicações（电信研究所）；Facebook AI（脸书人工智能研究院）；IQT Labs（IQT实验室）；University of Sheffield（谢菲尔德大学）；Imperial College London（伦敦帝国学院）；Unbabel（Unbabel公司）；Johns Hopkins University（约翰霍普金斯大学）；University of Wolverhampton（伍尔弗汉普顿大学）
英文摘要：
We present MLQE-PE, a new dataset for Machine Translation (MT) Quality Estimation (QE) and Automatic Post-Editing (APE). The dataset contains eleven language pairs, with human labels for up to 10,000 translations per language pair in the following formats: sentence-level direct assessments and post-editing effort, and word-level good/bad labels. It also contains the post-edited sentences, as well as titles of the articles where the sentences were extracted from, and the neural MT models used to translate the text.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
