Non-projective Dependency-based Pre-Reordering with Recurrent Neural Network for Machine Translation

The quality of statistical machine translation performed with phrase based approaches can be increased by permuting the words in the source sentences in an order which resembles that of the target language. We propose a class of recurrent neural models which exploit sourceside dependency syntax features to reorder the words into a target-like order. We evaluate these models on the Germanto-English language pair, showing significant improvements over a phrase-based Moses baseline, obtaining a quality similar or superior to that of hand-coded syntactical reordering rules.

• Type: Journal
• Year: 2015
• Where: ACL
• Authors: Antonio Valerio Miceli Barone, Giuseppe Attardi
• Keywords: ACL 2015, Computational Linguistics