To Swap or Not to Swap? Exploiting Dependency Word Pairs for Reordering in Statistical Machine Translation
Table 1: The results of our reordering approach using sparse dependency swap (DS) features, in BLEU scores (%) compared to the baseline (Base), on which features are added (∗: significant at p<0.05; ∗∗: significant at p<0.01). We also show the results of prior reordering methods, i.e., dependency distortion penalty (+DDP) [Cherry2008], sparse dependency path features (+Path) [Chang et al.2009], the combination of both (+DDP+Path), as well as sparse reordering orientation features (+SHR) [Cherry2013]. For all systems involving DDP and other features, comparison is also made to the system with only DDP. (†: significant at p<0.05; ††: significant at p<0.01). Note: All systems involving DS always incorporate DDP.
['[BOLD] Dataset', '[BOLD] Base', '[BOLD]   [BOLD] +DDP', '[BOLD]   [BOLD] +Path', '[BOLD] +DDP+Path', '[BOLD]   [BOLD] +SHR', '[BOLD] Ours  [BOLD] +DDP+DS', '[BOLD] Ours  [BOLD] +DDP+Path+DS']
The distortion limit of all the systems is set to 14, which yields the best result on the development set for the baseline system. As shown in the table, the system with our DS features and DDP on top of the baseline is able to improve over the baseline system without and with DDP, by +1.09 and +0.93 BLEU points respectively. The individual contribution of the other dependency-based features (Path), without or with DDP, is inferior to our DS features. Nevertheless, coupling our DS features with Path features yields the best result (+1.21 and +1.05 BLEU points over the baseline without and with DDP).