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1
WMT18 Quality Estimation Shared Task Test Data
Specia, Lucia; Logacheva, Varvara; Blain, Frederic. - : University of Sheffield, 2018
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2
WMT18 Quality Estimation Shared Task Training and Development Data
Specia, Lucia; Logacheva, Varvara; Blain, Frederic. - : University of Sheffield, 2018
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3
A Report on the Complex Word Identification Shared Task 2018 ...
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4
Assessing Crosslingual Discourse Relations in Machine Translation ...
Smith, Karin Sim; Specia, Lucia. - : arXiv, 2018
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5
End-to-end Image Captioning Exploits Multimodal Distributional Similarity ...
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6
Defoiling Foiled Image Captions ...
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7
Object Counts! Bringing Explicit Detections Back into Image Captioning ...
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8
Text Simplification From Professionally Produced Corpora ...
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9
SimPA: A Sentence-Level Simplification Corpus for the Public Administration Domain ...
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10
SimPA: A Sentence-Level Simplification Corpus for the Public Administration Domain ...
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11
Text Simplification From Professionally Produced Corpora ...
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12
SimPA: A Sentence-Level Simplification Corpus for the Public Administration Domain ...
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13
SimPA: A Sentence-Level Simplification Corpus for the Public Administration Domain ...
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14
Findings of the WMT 2018 shared task on quality estimation
Specia, Lucia; Logacheva, Varvara; Blain, Frederic; Astudillo, Ramón; Martins, André. - : Association for Computational Linguistics, 2018
Abstract: © 2018 The Authors. Published by Association for Computational Linguistics. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: http://dx.doi.org/10.18653/v1/W18-6451 ; We report the results of the WMT18 shared task on Quality Estimation, i.e. the task of predicting the quality of the output of machine translation systems at various granularity levels: word, phrase, sentence and document. This year we include four language pairs, three text domains, and translations produced by both statistical and neural machine translation systems. Participating teams from ten institutions submitted a variety of systems to different task variants and language pairs. ; The data and annotations collected for Tasks 1, 2 and 3 was supported by the EC H2020 QT21 project (grant agreement no. 645452). The shared task organisation was also supported by the QT21 project, national funds through Fundacao para a Ciencia e Tecnologia (FCT), with references UID/CEC/50021/2013 and UID/EEA/50008/2013, and by the European Research Council (ERC StG DeepSPIN 758969). We would also like to thank Julie Beliao and the Unbabel Quality Team for coordinating the annotation of the dataset used in Task 4.
URL: http://hdl.handle.net/2436/623555
https://doi.org/10.18653/v1/W18-6451
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15
deepQuest: a framework for neural-based quality estimation
Ive, Julia; Blain, Frederic; Specia, Lucia. - : Association for Computational Linguistics, 2018
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16
Sheffield submissions for the WMT18 quality estimation shared task
In: 807 ; 813 (2018)
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