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Eye-Tracking Recordings from a Pilot Study of WMT-style MT Outputs Ranking

CreatorZelenina, Maria
CreatorDěchtěrenko, Filip
CreatorBojar, Ondřej
Date2016-04-01T07:22:22Z
dc.date.accessioned2021-07-25T12:06:52Z
dc.date.available2021-07-25T12:06:52Z
Identifierhttp://hdl.handle.net/11234/1-1679
dc.identifier.urihttps://linghub.org/handle/123456789/1042787
DescriptionThis package contains the eye-tracker recordings of 8 subjects evaluating English-to-Czech machine translation quality using the WMT-style ranking of sentences. We provide the set of sentences evaluated, the exact screens presented to the annotators (including bounding box information for every area of interest and even for individual letters in the text) and finally the raw EyeLink II files with gaze trajectories. The description of the experiment can be found in the paper: Ondřej Bojar, Filip Děchtěrenko, Maria Zelenina. A Pilot Eye-Tracking Study of WMT-Style Ranking Evaluation. Proceedings of the LREC 2016 Workshop “Translation Evaluation – From Fragmented Tools and Data Sets to an Integrated Ecosystem”, Georg Rehm, Aljoscha Burchardt et al. (eds.). pp. 20-26. May 2016, Portorož, Slovenia. This work has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement no. 645452 (QT21). This work was partially financially supported by the Government of Russian Federation, Grant 074-U01. This work has been using language resources developed, stored and distributed by the LINDAT/CLARIN project of the Ministry of Education, Youth and Sports of the Czech Republic (project LM2010013).
PublisherCharles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)
Rightshttp://creativecommons.org/licenses/by-sa/4.0/
RightsCreative Commons - Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
SubjectMT evaluation
Subjecteye-tracking
TitleEye-Tracking Recordings from a Pilot Study of WMT-style MT Outputs Ranking
Typecorpus
TypeText
dcterms.available2016-04-01T07:22:22Z
dcterms.bibliographicCitationhttp://hdl.handle.net/11234/1-1679
dcterms.creatorZelenina, Maria
dcterms.creatorDěchtěrenko, Filip
dcterms.creatorBojar, Ondřej
dcterms.date2016-04-01T07:22:22Z
dcterms.descriptionThis package contains the eye-tracker recordings of 8 subjects evaluating English-to-Czech machine translation quality using the WMT-style ranking of sentences. We provide the set of sentences evaluated, the exact screens presented to the annotators (including bounding box information for every area of interest and even for individual letters in the text) and finally the raw EyeLink II files with gaze trajectories. The description of the experiment can be found in the paper: Ondřej Bojar, Filip Děchtěrenko, Maria Zelenina. A Pilot Eye-Tracking Study of WMT-Style Ranking Evaluation. Proceedings of the LREC 2016 Workshop “Translation Evaluation – From Fragmented Tools and Data Sets to an Integrated Ecosystem”, Georg Rehm, Aljoscha Burchardt et al. (eds.). pp. 20-26. May 2016, Portorož, Slovenia. This work has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement no. 645452 (QT21). This work was partially financially supported by the Government of Russian Federation, Grant 074-U01. This work has been using language resources developed, stored and distributed by the LINDAT/CLARIN project of the Ministry of Education, Youth and Sports of the Czech Republic (project LM2010013).
dcterms.identifierhttp://hdl.handle.net/11234/1-1679
dcterms.publisherCharles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)
dcterms.rightshttp://creativecommons.org/licenses/by-sa/4.0/
dcterms.rightsCreative Commons - Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
dcterms.subjectMT evaluation
dcterms.subjecteye-tracking
dcterms.titleEye-Tracking Recordings from a Pilot Study of WMT-style MT Outputs Ranking
dcterms.typecorpus
dcterms.typeText
odrl.Policyhttp://purl.org/net/rdflicense/cc-by-sa4.0


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Horizon 2020

This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 825182.