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- W2808505029 abstract "Training statistical machine translation systems used to require heavy computation times. It has been shown that approximations in the probabilistic approach could lead to impressing improvements (Fast align). We show that, by leveraging the advantages of the associative approach, we achieve similar, even faster, training times, while keeping comparable BLEU scores. Our contributions are of two types: of the engineering type, by introducing multi-processing both in sampling-based alignment and hierarchical sub-sentential alignment; of modeling type, by introducting approximations in hierarchical sub-sentential alignment that lead to important reductions in time without affecting the alignments produced. We test and compare our improvements on six typical language pairs of the Europarl corpus." @default.
- W2808505029 created "2018-06-21" @default.
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- W2808505029 date "2018-01-01" @default.
- W2808505029 modified "2023-09-26" @default.
- W2808505029 title "Leveraging the Advantages of Associative Alignment Methods for PB-SMT Systems" @default.
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- W2808505029 doi "https://doi.org/10.1007/978-3-319-93782-3_16" @default.
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