Statistical post-editing of a rule-based machine translation system
NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
Statistical post-editing on SYSTRAN's rule-based translation system
StatMT '07 Proceedings of the Second Workshop on Statistical Machine Translation
The efficacy of human post-editing for language translation
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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Translation memory tools now offer the translator to insert post-edited machine translation segments for which no match is found in the databases. The Google Translator Toolkit does this by default, advising in its Settings window: "Most users should not modify this". Post-editing of no matches appears to work on engines trained with specific bilingual data on a source written under controlled language constraints. Would this, however, work for any type of task as Google's advice implies? We have tested this by carrying out experiments with English---Chinese trainees, using the Toolkit to translate from the source text (the control group) and by post-editing (the experimental group). Results show that post-editing gains in productivity are marginal. With regard to quality, however, post-editing produces significantly better statistical results compared to translating manually. These gains in quality are observed independently of language direction, text difficulty or translator's level of performance. In light of these findings, we discuss whether translators should consider post-editing as a viable alternative to conventional translation.