MT for subtitling: User evaluation of post-editing productivity

no code implementations • EAMT 2020 • Maarit Koponen, Umut Sulubacak, Kaisa Vitikainen, Jörg Tiedemann

This paper presents a user evaluation of machine translation and post-editing for TV subtitles.

Machine Translation Translation

MT for Subtitling: Investigating professional translators’ user experience and feedback

no code implementations • AMTA 2020 • Maarit Koponen, Umut Sulubacak, Kaisa Vitikainen, Jörg Tiedemann

DiHuTra: a Parallel Corpus to Analyse Differences between Human Translations

1 code implementation • LREC 2022 • Ekaterina Lapshinova-Koltunski, Maja Popović, Maarit Koponen

The resulting corpus consists of English news and reviews source texts, their translations into Russian and Croatian, and translations of the reviews into Finnish.

Machine Translation Translation

LITHME: Language in the Human-Machine Era

no code implementations • EAMT 2022 • Maarit Koponen, Kais Allkivi-Metsoja, Antonio Pareja-Lora, Dave Sayers, Márta Seresi

The LITHME COST Action brings together researchers from various fields of study focusing on language and technology.

The WMT'18 Morpheval test suites for English-Czech, English-German, English-Finnish and Turkish-English

no code implementations • WS 2018 • Franck Burlot, Yves Scherrer, Vinit Ravishankar, Ond{\v{r}}ej Bojar, Stig-Arne Gr{\"o}nroos, Maarit Koponen, Tommi Nieminen, Fran{\c{c}}ois Yvon

Progress in the quality of machine translation output calls for new automatic evaluation procedures and metrics.

Machine Translation Translation

Comparing human perceptions of post-editing effort with post-editing operations

no code implementations • WS 2012 • Maarit Koponen

Machine Translation