@inproceedings{schutz-2008-artificial,
    title = "Artificial Cognitive {MT} Post-Editing Intelligence",
    author = {Sch{\"u}tz, J{\"o}rg},
    booktitle = "Proceedings of the 8th Conference of the Association for Machine Translation in the Americas: Government and Commercial Uses of MT",
    month = oct # " 21-25",
    year = "2008",
    address = "Waikiki, USA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2008.amta-govandcom.22/",
    pages = "448--453",
    abstract = "Post-editing (PE) is a necessary process in every MT deployment environment. The compe{\-}tences needed for PE are traditionally seen as a subset of a human translator`s competence. Meanwhile, some companies are accepting that the PE process involves self-standing linguistic tasks, which need their own training efforts and appropriate software tool support. To date, we still lack recorded qualitatively and quantitatively PE user-activity data that adequately describe the tasks and in particular the human cognitive processes accomplished. This data is needed to effectively model, de{\-}sign and implement supportive software sys{\-}tems which, on the one hand, efficiently guide the human post-editor and enhance her cogni{\-}tive capabilities, and on the other hand, have a certain influence on the translation perfor{\-}mance and competence of the employed MT system. In this paper we argue for a frame{\-}work of practices to describe the PE process by correlating data obtained in laboratory ex{\-}periments and augmented by additional data from different resources such as interviews and mathematical prediction models with the tasks fulfilled, and to model the identified pro{\-}cess in a multi-facetted fashion as a basis for the implementation of a human PE-aware in{\-}teractive software system."
}