MLQE-PE: A Multilingual Quality Estimation and Post-Editing Dataset

October 09, 2020 ยท Declared Dead ยท ๐Ÿ› International Conference on Language Resources and Evaluation

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Authors Marina Fomicheva, Shuo Sun, Erick Fonseca, Chrysoula Zerva, Frรฉdรฉric Blain, Vishrav Chaudhary, Francisco Guzmรกn, Nina Lopatina, Lucia Specia, Andrรฉ F. T. Martins arXiv ID 2010.04480 Category cs.CL: Computation & Language Citations 77 Venue International Conference on Language Resources and Evaluation Last Checked 4 months ago
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.
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