This paper presents a study of user-perceived vs real machine translation (MT) post-editing effort and productivity gains, focusing on two bidirectional language pairs: English— German and English—Dutch. Twenty experienced media professionals post-edited statistical MT output and also manually translated comparative texts within a production environment. The paper compares the actual post-editing time against the users’ perception of the effort and time required to post-edit the MT output to achieve publishable quality, thus measuring real (vs perceived) productivity gains. Although for all the language pairs users perceived MT post-editing to be slower, in fact it proved to be a faster option than manual translation for two translation directions out of four, i.e. for Dutch→English, and (marginally) for English→German. For further objective scrutiny, the paper also checks the correlation of three state-of-the-art automatic MT evaluation metrics (BLEU, METEOR and TER) with the actual post-editing time.

Perception vs Reality: Measuring Machine Translation Post-Editing Productivity / Gaspari, F; Antonio, Toral; Sudip Kumar, Naskar; Declan, Groves; Andy, Way. - (2014), pp. 60-72. (Intervento presentato al convegno Third Workshop on Post-Editing Technology and Practice tenutosi a Vancouver, Canada nel 26 October 2014).

Perception vs Reality: Measuring Machine Translation Post-Editing Productivity

Gaspari F;
2014

Abstract

This paper presents a study of user-perceived vs real machine translation (MT) post-editing effort and productivity gains, focusing on two bidirectional language pairs: English— German and English—Dutch. Twenty experienced media professionals post-edited statistical MT output and also manually translated comparative texts within a production environment. The paper compares the actual post-editing time against the users’ perception of the effort and time required to post-edit the MT output to achieve publishable quality, thus measuring real (vs perceived) productivity gains. Although for all the language pairs users perceived MT post-editing to be slower, in fact it proved to be a faster option than manual translation for two translation directions out of four, i.e. for Dutch→English, and (marginally) for English→German. For further objective scrutiny, the paper also checks the correlation of three state-of-the-art automatic MT evaluation metrics (BLEU, METEOR and TER) with the actual post-editing time.
2014
Perception vs Reality: Measuring Machine Translation Post-Editing Productivity / Gaspari, F; Antonio, Toral; Sudip Kumar, Naskar; Declan, Groves; Andy, Way. - (2014), pp. 60-72. (Intervento presentato al convegno Third Workshop on Post-Editing Technology and Practice tenutosi a Vancouver, Canada nel 26 October 2014).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/894243
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