Our models improve translation quality over the single generic label approach of Chiang (2005) and perform on par with the syntactically motivated approach from Zollmann and Venugopal (2006) on the N IST large Chinese—to—English translation task.

Evaluated on a Chinese-to-English translation task, our approach improves translation quality over a popular PSCFG baseline—the hierarchical model of Chiang (2005) —and performs on par

We evaluate our approach by comparing translation quality , as evaluated by the IBM-BLEU (Papineni et al., 2002) metric on the NIST Chinese-to-English translation task using MT04 as development set to train the model parameters A, and MTOS, MT06 and MT08 as test sets.

In line with previous findings for syntax-augmented grammars (Zollmann and V0-gel, 2010), the source-side-based grammar does not reach the translation quality of its target-based counterpart; however, the model still outperforms the hi-

(2008), the impact of these rules on translation quality is negligible.

Label-based approaches have resulted in improvements in translation quality over the single X label approach (Zollmann et al., 2008; Mi and Huang, 2008); however, all the works cited here rely on stochastic parsers that have been trained on manually created syntactic treebanks.

by interpolating them with less sparse ones, could in the future lead to an additional increase in translation quality .

These extra features assess translation quality past the synchronous grammar derivation and learning general reordering or word emission preferences for the language pair.

By advancing from structures which mimic linguistic syntax, to learning linguistically aware latent recursive structures targeting translation, we achieve significant improvements in translation quality for 4 different language pairs in comparison with a strong hierarchical translation baseline.

Early work in statistical phrase-based translation considered whether restricting translation models to use only syntactically well-formed constituents might improve translation quality (Koehn et al., 2003) but found such restrictions failed to improve translation quality .

The translation quality significantly improved on a constrained task, and the perplexity improvements suggest that interpolating between 71- gram and syntactic LMs may hold promise on larger data sets.

By increasing the beam size and distortion limit of the baseline system, future work may examine whether a baseline system with comparable runtimes can achieve comparable translation quality .

We propose a novel technique of learning how to transform the source parse trees to improve the translation qualities of syntax-based translation models using synchronous context-free grammars.

We use BLEU (Papineni et al., 2002) and TER (Snover et al., 2006) to evaluate translation qualities .

We integrate the neighboring context of the subgraph in our transformation preference predictions, and this improve translation qualities further.