: NLP Vocabulary: data-driven machine translation

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data-driven machine translation

Machine translation approach that relies on large amounts of parallel corpora to learn statistical models of translation.

The quality of data-driven machine translation (MT) mostly depends on the size of parallel data available for training. (Leveling, Ganguly, Dandapat & Jones, 2012)
The widespread adoption of data-driven MT since the 2000s (Kenny 2020) has brought considerable change to the industry and professional translators are increasingly being asked to carry out PEMT tasks. (Brockmann, Wiesinger & Ciobanu, 2022)
This shows that our method can be useful not only to development of semi-automatic lexicon for data-driven machine translation but also to annotation of corresponding subsequences in translation memory system. (Yamamoto, Kudo, Tsuboi & Matsumoto, 2003)
What we propose is a novel approach that exploits the regular nature of Arabic morphology to increase the quality and coverage of data-driven machine translation. (Phillips, Cavalli-Sforza & Brown, 2007)
While today almost all data-driven MT implement some version of this 3-step schema making use of 'previous' translation knowledge (i.e. sets of bitexts) it is largely unclear what and how human translators actually proceed what chunks they decompose and how they are re-composed into a target sentence. (Carl, Jakobsen & Jensen, 2008)

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