Simple and Effective Approach for Consistent Training of Hierarchical Phrase-based Translation Models

Abstract

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.