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MLQE-PE: A multilingual quality estimation and post-editing dataset
Fomicheva, Marina Sun, Shuo Fonseca, Erick Zerva, Chrysoula Blain, Frédéric Chaudhary, Vishrav Guzmán, Francisco Lopatina, Nina Specia, Lucia Martins, André FT
Fomicheva, Marina
Sun, Shuo
Fonseca, Erick
Zerva, Chrysoula
Blain, Frédéric
Chaudhary, Vishrav
Guzmán, Francisco
Lopatina, Nina
Specia, Lucia
Martins, André FT
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2020-10-11
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2010.04480v3.pdf
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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.
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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]
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en
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© 2020 The Authors. For reuse permissions, please contact the Authors.