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.
@article{arxiv.2010.04480,
title = {MLQE-PE: A Multilingual Quality Estimation and Post-Editing Dataset},
author = {Marina Fomicheva and Shuo Sun and Erick Fonseca and Chrysoula Zerva and Frédéric Blain and Vishrav Chaudhary and Francisco Guzmán and Nina Lopatina and Lucia Specia and André F. T. Martins},
journal= {arXiv preprint arXiv:2010.04480},
year = {2021}
}