Tuning Phrase-Based Segmented Translation for a Morphologically Complex Target Language

By Stig-Arne Grönroos, Sami Virpioja, Mikko Kurimo. 2015

4 citations recorded by the source corpus.

This article describes the Aalto University entry to the English-to-Finnish shared translation task in WMT 2015. The system participates in the constrained condition, but in addition we impose some further constraints, using no language-specific resources beyond those provided in the task. We use a morphological segmenter, Morfessor FlatCat, but train and tune it in an unsupervised manner. The system could thus be used for another language pair with a morphologically complex target language, without needing modification or additional resources.

Subfields: Artificial Intelligence

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In Artificial Intelligence

1. Mikko Kurimo
2. Stig-Arne Grönroos
3. Sami Virpioja

Citations in the corpus

• Hybrid Morphological Segmentation for Phrase-Based Machine Translation (2016, methodology): “Omorfi-restricted Morfessor was tuned following Grönroos et al. (2015) to bring the number of to-kens on the Finnish target side as close as possible to the English source side.”