Phrase-Based SMT for Finnish with More Data, Better Models and Alternative Alignment and Translation Tools
By Jörg Tiedemann, Fabienne Cap, Jenna Kanerva, Filip Ginter, Sara Stymne, Robert Östling, Marion Weller-Di Marco. 2016
13 citations recorded by the source corpus.
This paper summarises the contributions of the teams at the University of Helsinki, Uppsala University and the University of Turku to the news translation tasks for translating from and to Finnish. Our models address the problem of treating morphology and data coverage in various ways. We introduce a new efficient tool for word alignment and discuss factorisations, gappy language models and reinflection techniques for generating proper Finnish output. The results demonstrate once again that training data is the most effective way to increase translation performance.
Subfields: Artificial Intelligence
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In Artificial Intelligence
- Ondrej Bojar
- Philipp Koehn
- Christof Monz
- Yvette Graham
- Barry Haddow
- Graham Neubig
- Felix Stahlberg
- Rico Sennrich
Citations in the corpus
- Findings of the 2018 Conference on Machine Translation (WMT18) (2018, methodology): “HY-SMT (Tiedemann et al., 2016) is the Helsinki SMT system submitted at WMT 2016 (the constrained-basic+back-translated version).”