@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://aclanthology.org/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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Artificial Cognitive MT Post-Editing Intelligence
%A Schütz, Jörg
%S Proceedings of the 8th Conference of the Association for Machine Translation in the Americas: Government and Commercial Uses of MT
%D 2008
%8 oct" 21 25"
%I Association for Machine Translation in the Americas
%C Waikiki, USA
%F schutz-2008-artificial
%X 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.
%U https://aclanthology.org/2008.amta-govandcom.22
%P 448-453
Markdown (Informal)
[Artificial Cognitive MT Post-Editing Intelligence](https://aclanthology.org/2008.amta-govandcom.22) (Schütz, AMTA 2008)
ACL
- Jörg Schütz. 2008. Artificial Cognitive MT Post-Editing Intelligence. In Proceedings of the 8th Conference of the Association for Machine Translation in the Americas: Government and Commercial Uses of MT, pages 448–453, Waikiki, USA. Association for Machine Translation in the Americas.