Combining word-level and character-level models for machine translation between closely-related languages

Authors:
Preslav Nakov;Jörg Tiedemann
Affiliations:
Qatar Computing Research Institute, Doha, Qatar;Uppsala University, Uppsala, Sweden
Venue:
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
Year:
2012

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Abstract

We propose several techniques for improving statistical machine translation between closely-related languages with scarce resources. We use character-level translation trained on n-gram-character-aligned bitexts and tuned using word-level BLEU, which we further augment with character-based transliteration at the word level and combine with a word-level translation model. The evaluation on Macedonian-Bulgarian movie subtitles shows an improvement of 2.84 BLEU points over a phrase-based word-level baseline.