Translation Quality and Productivity: A Study on Rich Morphology Languages

Lucia Specia, Kim Harris, Frédéric Blain, Aljoscha Burchardt, Vivien Macketanz, Inguna Skadiņa, Matteo Negri, and Marco Turchi

In: Machine Translation Summit XVI. Machine Translation Summit (MT-Summit) Nagoya, Japan Pages 55-71 Asia-Pacific Association for Machine Translation 2017.


This paper introduces a unique large-scale machine translation dataset with various levels of human annotation combined with automatically recorded productivity features such as time and keystroke logging and manual scoring during the annotation process. The data was collected as part of the EU-funded QT21 project and comprises 20,000–45,000 sentences of industry-generated content with translation into English and three morphologically rich languages: English–German/Latvian/Czech and German–English, in either the information technology or life sciences domain. Altogether, the data consists of 176,476 tuples including a source sentence, the respective machine translation by a statistical system (additionally, by a neural system for two language pairs), a post-edited version of such translation by a native-speaking professional translator, an independently created reference translation, and information on post-editing: time, keystrokes, Likert scores, and annotator identifier. A subset of 2,000 sentences from this data per language pair and system type was also manually annotated with translation errors for deeper linguistic analysis. We describe the data collection process, provide a brief analysis of the resulting annotations and discuss the use of the data in quality estimation and automatic post-editing tasks.


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German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz