We study the challenges raised by Arabic verb and subject detection and reordering in Statistical Machine Translation (SMT). We show that post-verbal subject (VS) constructions are hard to translate because they have highly ambiguous reordering patterns when translated to English. In addition, implementing reordering is difficult because the boundaries of VS constructions are hard to detect accurately, even with a state-of-the-art Arabic dependency parser. We therefore propose to reorder VS constructions into SV order for SMT word alignment only. This strategy significantly improves BLEU and TER scores, even on a strong large-scale baseline and despite noisy parses. 1

We propose a novel source-side dependency tree reordering model for statistical machine translation, in which subtree movements and constraints are represented as reordering events associated with the widely used lexicalized reordering models. This model allows us to not only efficiently capture the statistical distribution of the subtree-to-subtree transitions in training data, but also utilize it directly at the decoding time to guide the search process. Using subtree movements and constraints as features in a log-linear model, we are able to help the reordering models make better selections. It also allows the subtle importance of monolingual syntactic movements to be learned alongside other reordering features. We show improvements in translation quality in English→Spanish and English→Iraqi translation tasks. 1

What empirical evidence is there that adding syntactic constraints to MT decoding particular, PMT decoding will lead to improvements in translation quality? Your proposal claims that your method for adding syntactic constraints will result not only in a more complete search of the space of string permutations involved in PMT but also in an improved ability to discriminate between good and bad translations. In Section 3 you claim that the ability to account for syntactically governed re-ordering patterns is an advantage and in Section 4 you claim, on the basis of a constructed example, that your proposed method will improve quality by removing ungrammatical but high scoring distractor analyses, and that the completeness of the search will be improved by reducing the need for aggressive heuristics about re-ordering. Do you anticipate that separate constraints on re-ordering will still be required? If not, say why not. If so, brie y sketch how these constraints will be implemented and the means by which they will interact with the new syntactic constraints. Statistical MT (SMT) systems are based on the source-channel model of communication (Weaver, 1949; Brown et al., 1993, 1990) whereby an output string is modelled as being