A Productivity Test of Statistical Machine Translation Post-Editing in a Typical Localisation Context

By Mirko Plitt, François Masselot. Prague Bulletin of Mathematical Linguistics, 2010

299 citations recorded by the source corpus.

A Productivity Test of Statistical Machine Translation Post-Editing in a Typical Localisation Context We evaluated the productivity increase of statistical MT post-editing as compared to traditional translation in a two-day test involving twelve participants translating from English to French, Italian, German, and Spanish. The test setup followed an empirical methodology. A random subset of the entire new content produced in our company during a given year was translated with statistical MT engines trained on data from the previous year. The translation environment recorded translation and post-editing times for each sentence. The results show a productivity increase for each participant, with significant variance across inviduals.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/f3748481acfacd7065af5233db4b77f9c5f8f000

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In Artificial Intelligence

Citations in the corpus

“This sentence is wrong.” Detecting errors in machine-translated sentences (2011, methodology): “The experimental protocol is inspired by the one described in (Plitt and Masselot, 2010).”
Machine Translation and Monolingual Postediting: The AFRL WMT-14 System (2014, background): “The use of basic postediting tools by bilingual human translators has been shown to yield substantial increases in terms of productivity (Plitt and Masselot, 2010) as well as improvements in translation quality (Green et al.”
Modelling Annotator Bias with Multi-task Gaussian Processes: An Application to Machine Translation Quality Estimation (2013, background): “2011), or simply averaged across multiple translators (Plitt and Masselot, 2010), both strategies far from ideal.”
USAAR: An Operation Sequential Model for Automatic Statistical Post-Editing (2016, background): “A number of studies con-ﬁrm that post-editing MT output improves trans-lators’ performance in terms of productivity and it may also impact translation quality and consistency (Guerberof, 2009; Plitt and Masselot, 2010; Zampieri and Vela, 2014).”