Reordering is a serious challenge in statistical machine translation. We propose a method for analysing syntactic reordering in parallel corpora and apply it to understanding the differences in the performance of SMT systems. Results at recent large-scale evaluation campaigns show that synchronous grammar-based statistical machine translation models produce superior results for language pairs such as Chinese to English. However, for language pairs such as Arabic to English, phrase-based approaches continue to be competitive. Until now, our understanding of these results has been limited to differences in Bleu scores. Our analysis shows that current state-of-the-art systems fail to capture the majority of reorderings found in real data.
['Alexandra Birch', 'Phil Blunsom', 'Miles Osborne']
A Quantitative Analysis of Reordering Phenomena