MLQE-PE: A multilingual quality estimation and post-editing dataset
Average rating
Cast your vote
You can rate an item by clicking the amount of stars they wish to award to this item.
When enough users have cast their vote on this item, the average rating will also be shown.
Star rating
Your vote was cast
Thank you for your feedback
Thank you for your feedback
Authors
Fomicheva, MarinaSun, Shuo
Fonseca, Erick
Zerva, Chrysoula
Blain, Frédéric
Chaudhary, Vishrav
Guzmán, Francisco
Lopatina, Nina
Specia, Lucia
Martins, André FT
Issue Date
2020-10-11
Metadata
Show full item recordAbstract
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.Citation
Fomicheva, M., Sun, S., Fonseca, E.R. et al. (2020) MLQE-PE : a multilingual quality estimation and post-editing dataset. arXiv:2010.04480v3 [cs.CL]Publisher
arXivAdditional Links
https://arxiv.org/abs/2010.04480Type
Working paperLanguage
enDescription
© 2020 The Authors. For reuse permissions, please contact the Authors.