WMT16 Quality Estimation Shared Task Training and Development Data

  Authors

Specia, Lucia ; Logacheva, Varvara and Scarton, Carolina

  Item identifier

http://hdl.handle.net/11372/LRT-1646

 Project URL

http://www.statmt.org/wmt16/quality-estimation-task.html

 Date issued

2016-02-29

 Type

corpus, text

 Size

3.59 mb

 Language(s)

English , German

 Description

Training and development data for the WMT16 QE task. Test data will be published as a separate item. This shared task will build on its previous four editions to further examine automatic methods for estimating the quality of machine translation output at run-time, without relying on reference translations. We include word-level, sentence-level and document-level estimation. The sentence and word-level tasks will explore a large dataset produced from post-editions by professional translators (as opposed to crowdsourced translations as in the previous year). For the first time, the data will be domain-specific (IT domain). The document-level task will use, for the first time, entire documents, which have been human annotated for quality indirectly in two ways: through reading comprehension tests and through a two-stage post-editing exercise. Our tasks have the following goals: - To advance work on sentence and word-level quality estimation by providing domain-specific, larger and professionally annotated datasets. - To study the utility of detailed information logged during post-editing (time, keystrokes, actual edits) for different levels of prediction. - To analyse the effectiveness of different types of quality labels provided by humans for longer texts in document-level prediction. This year's shared task provides new training and test datasets for all tasks, and allows participants to explore any additional data and resources deemed relevant. A in-house MT system was used to produce translations for the sentence and word-level tasks, and multiple MT systems were used to produce translations for the document-level task. Therefore, MT system-dependent information will be made available where possible.

 Publisher

University of Sheffield

 Acknowledgement

European Union

Project code: H2020-ICT-2014-1-645452

Project name: QT21: Quality Translation 21

 Subject(s)

machine translation quality estimation machine learning

 Collection(s)

LRT + Open Submissions Data & Tools

 

This item is replaced by a newer submission:

http://hdl.handle.net/11372/LRT-1974

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