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Intelligent Speech Features Mining for Robust Synthesis System Evaluation Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Leveraging the Advantages of Associative Alignment Methods for PB-SMT Systems

Authors : Baosong Yang, Yves Lepage

Published in: Human Language Technology. Challenges for Computer Science and Linguistics

Publisher: Springer International Publishing

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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.

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Footnotes
4
Thanks to the authors for providing the source code.
 
5
train-model.perl –first step 4 .
 
6
Anymalign is an anytime process, and should be given a timeout.
 
7
Notice that, by definition: \( \mathrm{Ncut} (X, Y) = \mathrm{Ncut} (\bar{X}, \bar{Y}) \) and \( \mathrm{Ncut} (X, \bar{Y}) = \mathrm{Ncut} (\bar{X}, Y) \). The same holds for \( \mathrm{cut} \).
 
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Metadata
Title
Leveraging the Advantages of Associative Alignment Methods for PB-SMT Systems
Authors
Baosong Yang
Yves Lepage
Copyright Year
2018
Book
Human Language Technology. Challenges for Computer Science and Linguistics

Print ISBN: 978-3-319-93781-6

Electronic ISBN: 978-3-319-93782-3

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-93782-3_16

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