Integrating Online and Active Learning in a Computer-Assisted Translation Workbench

Vicent Alabau, Jésus González-Rubio, Daniel Ortíz-Martínez , Francisco Casacuberta, Mercedes Garcia Martinez, Bartolomé Mesa-Lao, Dan Cheung Petersen, Barbara Dragsted, Michael Carl

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    This paper describes a pilot study with a computed-assisted translation workbench aiming at testing the integration of online and active learning features. We investigate the effect of these features on translation productivity, using interactive translation prediction (ITP) as a baseline. User activity data were collected from five beta testers using key-logging and eye-tracking. User feedback was also collected at the end of the experiments in the form of retrospective think-aloud protocols. We found that OL performs better than ITP, especially in terms of translation speed. In addition, AL provides better translation quality than ITP for the same levels of user effort. We plan to incorporate these features in the final version of the workbench.
    Original languageEnglish
    Title of host publicationProceedings of the Workshop on Interactive and Adaptive Machine Translation
    EditorsFrancisco Casacuberta, Marcello Federico, Philipp Koehn
    Number of pages8
    PublisherAssociation for Machine Translation in the Americas (AMTA)
    Publication date2014
    Publication statusPublished - 2014
    EventThe 11th Conference of the Association for Machine Translation in the Americas 2014 - Vancouver, Canada
    Duration: 22 Oct 201426 Oct 2014
    Conference number: 11


    ConferenceThe 11th Conference of the Association for Machine Translation in the Americas 2014
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