Preference Grammars: Softening Syntactic Constraints to Improve Statistical Machine Translation

We propose a novel probabilistic synchoronous context-free grammar formalism for statistical machine translation, in which syntactic nonterminal labels are represented as “soft” preferences rather than as “hard” matching constraints. This formalism allows us to efficiently score unlabeled synchronous derivations without forgoing traditional syntactic constraints. Using this score as a feature in a log-linear model, we are able to approximate the selection of the most likely unlabeled derivation. This helps reduce fragmentation of probability across differently labeled derivations of the same translation. It also allows the importance of syntactic preferences to be learned alongside other features (e.g., the language model) and for particular labeling procedures. We show improvements in translation quality on small and medium sized Chinese-to-English translation tasks.