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(2015)Fourth Workshop on Post-Editing Technology and Practice, Proceedings.p.31-45 Mark abstract In this paper, we report on a post-editing study for general text types from English into Dutch conducted with master's students of translation. We used a fine-grained machine translation (MT) quality assessment method with error weights that correspond to severity levels and are related to cognitive load. Linear mixed effects models are applied to analyze the impact of MT quality on potential post-editing effort indicators. The impact of MT quality is evaluated on three different levels, each with an increasing granularity. We find that MT quality is a significant predictor of all different types of post-editing effort indicators and that different types of MT errors predict different post-editing effort indicators.

Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-6990271



Author: Joke Daems , Sonia Vandepitte , Robert Hartsuiker and Lieve Macken

Source: https://biblio.ugent.be/publication/6990271



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