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The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation ...
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LAWDR: Language-Agnostic Weighted Document Representations from Pre-trained Models ...
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Classification-based Quality Estimation: Small and Efficient Models for Real-world Applications ...
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Improving Zero-Shot Translation by Disentangling Positional Information ...
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As Easy as 1, 2, 3: Behavioural Testing of NMT Systems for Numerical Translation ...
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Putting words into the system's mouth: A targeted attack on neural machine translation using monolingual data poisoning ...
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Detecting Hallucinated Content in Conditional Neural Sequence Generation ...
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Alternative Input Signals Ease Transfer in Multilingual Machine Translation ...
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Improving Zero-Shot Translation by Disentangling Positional Information ...
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XLEnt: Mining a Large Cross-lingual Entity Dataset with Lexical-Semantic-Phonetic Word Alignment ...
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Adapting High-resource NMT Models to Translate Low-resource Related Languages without Parallel Data ...
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Improving Zero-Shot Translation by Disentangling Positional Information
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Massively Multilingual Document Alignment with Cross-lingual Sentence-Mover's Distance ...
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MLQE-PE: A Multilingual Quality Estimation and Post-Editing Dataset ...
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Improving Zero-Shot Translation by Disentangling Positional Information ...
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Unsupervised quality estimation for neural machine translation
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In: 8 ; 539 ; 555 (2020)
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An exploratory study on multilingual quality estimation
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In: 366 ; 377 (2020)
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BERGAMOT-LATTE submissions for the WMT20 quality estimation shared task
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In: 1010 ; 1017 (2020)
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Findings of the WMT 2020 shared task on quality estimation
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In: 743 ; 764 (2020)
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MLQE-PE: A multilingual quality estimation and post-editing dataset
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Fomicheva, Marina; Sun, Shuo; Fonseca, Erick; Zerva, Chrysoula; Blain, Frédéric; Chaudhary, Vishrav; Guzmán, Francisco; Lopatina, Nina; Specia, Lucia; Martins, André FT. - : arXiv, 2020
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Abstract:
© 2020 The Authors. For reuse permissions, please contact the Authors. ; 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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Keyword:
cs.CL; machine translation
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URL: http://hdl.handle.net/2436/624591
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