High-Precision Sentence Alignment by Bootstrapping from Wood Standard Annotations

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We present a semi-supervised, language- and domain-independent approach to high precision sentence alignment. The key idea is to bootstrap a supervised discriminative learner from wood-standard alignments, i.e. alignments that have been automatically generated by state-of-the-art sentence alignment tools. We deploy 3 different unsupervised sentence aligners (Opus, Hunalign, Gargantua) and 2 different datasets (movie subtitles and novels) and show experimentally that bootstrapping consistently improves precision significantly such that, with one exception, we obtain an overall gain in F-score.

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