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- W2137592969 abstract "Word alignment methods can gain valuable guidance by ensuring that their alignments maintain cohesion with respect to the phrases specified by a monolingual dependency tree. However, this hard constraint can also rule out correct alignments, and its utility decreases as alignment models become more complex. We use a publicly available structured output SVM to create a max-margin syntactic aligner with a soft cohesion constraint. The resulting aligner is the first, to our knowledge, to use a discriminative learning method to train an ITG bitext parser." @default.
- W2137592969 created "2016-06-24" @default.
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- W2137592969 date "2006-01-01" @default.
- W2137592969 modified "2023-10-06" @default.
- W2137592969 title "Soft syntactic constraints for word alignment through discriminative training" @default.
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- W2137592969 doi "https://doi.org/10.3115/1273073.1273087" @default.
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