English-to-Czech Factored Machine Translation
By Ondrej Bojar. 2007
54 citations recorded by the source corpus.
This paper describes experiments with English-to-Czech phrase-based machine translation. Additional annotation of input and output tokens (multiple factors) is used to explicitly model morphology. We vary the translation scenario (the setup of multiple factors) and the amount of information in the morphological tags. Experimental results demonstrate significant improvement of translation quality in terms of BLEU.
Subfields: Artificial Intelligence, Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Philipp Koehn
- Chris Callison-Burch
- Christof Monz
- Hieu D. Hoang
- Holger Schwenk
- Ondrej Dusek
- Josh Schroeder
- Filip Jurvc'ivcek
In Natural Language Processing
- Philipp Koehn
- Christof Monz
- Chris Callison-Burch
- Ondrej Dusek
- Hieu D. Hoang
- Barry Haddow
- Ondrej Bojar
- Nadir Durrani
Citations in the corpus
- Neural Generation for Czech: Data and Baselines (2019, positive): “This approach is inspired by similar approaches in phrase-based MT (Bojar, 2007; Toutanova et al., 2008; Fraser, 2009) and was developed in parallel to recent similar experiments with two-step neural MT (Nadejde et al.”
- (Meta-) Evaluation of Machine Translation (2007, background): “, 2007) cu Charles University, Czech Republic (Bojar, 2007) limsi LIMSI-CNRS, France (Schwenk, 2007) liu University of Linköping, Sweden(Holmqvist et al.”
- Factored Translation Models (2007, methodology): “Factored translation models have also been used for the integration of CCG supertags (Birch et al., 2007), domain adaptation (Koehn and Schroeder, 2007) and for the improvement of English-Czech translation (Bojar, 2007).”