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    MLQE-PE: A multilingual quality estimation and post-editing dataset

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    Authors
    Fomicheva, Marina
    Sun, Shuo
    Fonseca, Erick
    Zerva, Chrysoula
    Blain, Frédéric
    Chaudhary, Vishrav
    Guzmán, Francisco
    Lopatina, Nina
    Specia, Lucia
    Martins, André FT
    Issue Date
    2020-10-11
    
    Metadata
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    Abstract
    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
    arXiv
    URI
    http://hdl.handle.net/2436/624591
    Additional Links
    https://arxiv.org/abs/2010.04480
    Type
    Working paper
    Language
    en
    Description
    © 2020 The Authors. For reuse permissions, please contact the Authors.
    Collections
    Research Institute in Information and Language Processing

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