Productivity and quality in the post-editing of outputs from translation memories and machine translation
نویسنده
چکیده
Machine-translated segments are increasingly included as fuzzy matches within the translation-memory systems in the localisation workflow. This study presents preliminary results on the correlation between these two types of segments in terms of productivity and final quality. In order to test these variables, we set up an experiment with a group of eight professional translators using an on-line post-editing tool and a statistical-based machine translation engine. The translators were asked to translate new, machine-translated and translation-memory segments from the 80-90 percent value range using a post-editing tool without actually knowing the origin of each segment, and to complete a questionnaire. The findings suggest that translators have higher productivity and quality when using machine-translated output than when processing fuzzy matches from translation memories. Furthermore, translators' technical experience seems to have an impact on productivity but not on quality.
منابع مشابه
Machine Translation for Human Translators
While machine translation is sometimes sufficient for conveying information across language barriers, many scenarios still require precise human-quality translation that MT is currently unable to deliver. Governments and international organizations such as the United Nations require accurate translations of content dealing with complex geopolitical issues. Community-driven projects such as Wiki...
متن کاملDeepfix: Statistical Post-editing of Statistical Machine Translation Using Deep Syntactic Analysis
Deepfix is a statistical post-editing system for improving the quality of statistical machine translation outputs. It attempts to correct errors in verb-noun valency using deep syntactic analysis and a simple probabilistic model of valency. On the English-to-Czech translation pair, we show that statistical post-editing of statistical machine translation leads to an improvement of the translatio...
متن کاملIdentifying the Machine Translation Error Types with the Greatest Impact on Post-editing Effort
Translation Environment Tools make translators' work easier by providing them with term lists, translation memories and machine translation output. Ideally, such tools automatically predict whether it is more effortful to post-edit than to translate from scratch, and determine whether or not to provide translators with machine translation output. Current machine translation quality estimation s...
متن کاملRelations between different types of post-editing operations, cognitive eddort and temporal effort
Despite the growing interest in and use of machine translation post-edited outputs, there is little research work exploring different types of post-editing operations, i.e. types of translation errors corrected by post-editing. This work investigates five types of post-edit operations and their relation with cognitive post-editing effort (quality level) and postediting time. Our results show th...
متن کاملStatistical Post-Editing of Machine Translation for Domain Adaptation
This paper presents a statistical approach to adapt out-of-domain machine translation systems to the medical domain through an unsupervised post-editing step. A statistical post-editing model is built on statistical machine translation (SMT) outputs aligned with their translation references. Evaluations carried out to translate medical texts from French to English show that an out-of-domain mac...
متن کامل