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- W2143134347 abstract "In this paper, we apply a method of unsupervised morphology learning to a state-of-the-art phrase-based statistical ma chine translation (SMT) system. In SMT, words are traditionally used as the smallest units of translation. Such a system generalizes poorl y to word forms that do not occur in the training data. In particular, this is problematic for languages that are highly compounding, highly inflecting, or both. An alternative way is to use sub-word units, such as morphemes. We use the Morfessor algorithm to find statistical mo rphemelike units (called morphs) that can be used to reduce the size of the lexicon and improve the ability to generalize. Transl ation and language models are trained directly on morphs instead of words. The approach is tested on three Nordic languages (Danish, Finnish, and Swedish) that are included in the Europarl corpus consisting of the Proceedings of the European Parliament. However, in our experiments we did not obtain higher BLEU scores for the morph model than for the standard word-based approach. Nonetheless, the proposed morph-based solution has clear benefits, as morpho logically well motivated structures (phrases) are learned , and the proportion of words left untranslated is clearly reduced." @default.
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- W2143134347 date "2007-09-10" @default.
- W2143134347 modified "2023-09-27" @default.
- W2143134347 title "Morphology-Aware Statistical Machine Translation Based on Morphs Induced in an Unsupervised Manner" @default.
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