Learning Morphological Normalization for Translation from and into Morphologically Rich Languages
Résumé
When translating between a morphologically rich language (MRL)
and English, word forms in the MRL often encode grammatical
information that is irrelevant with respect to English, leading to
data sparsity issues. This problem can be mitigated by removing from the MRL irrelevant
information through normalization.
Such preprocessing is usually performed in a deterministic fashion,
using hand-crafted rules and yielding suboptimal representations. We introduce here a simple way
to automatically compute an appropriate normalization of
the MRL and show that it can improve machine translation in both directions.
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