Simple and Effective Approach for Consistent Training of Hierarchical Phrase-based Translation Models
Stephan Peitz, David Vilar, Hermann Ney · 2014
In this paper, we present a simple approach for consistent training of hierarchical phrase-based translation models.In order to consistently train a translation model, we perform hierarchical phrasebased decoding on training data to find derivations between the source and target sentences.This is done by synchronous parsing the given sentence pairs.After extracting k-best derivations, we reestimate the translation model probabilities based on collected rule counts.We show the effectiveness of our procedure on the IWSLT German→English and English→French translation tasks.Our results show improvements of up to 1.6 points BLEU.