While phrase-based SMT models have been quite successful, they are nonetheless prone to production of syntactically disfluent or semantically odd translations, and much recent research has sought to exploit linguistic structure and patterns of meaning to improve translation quality. Two major approaches to syntactic transfer are hierarchical phrase-based translation and tree-to-string (TTS) transduction models.  Hierarchical phrase-based approaches use synchronous grammar rules, which simultaneously expand partial derivations of corresponding sentences in two languages. These are automatically induced from an aligned corpus, and the lowest hierarchical layer corresponds to phrase-to-phrase translation rules like those in ordinary phrase-based translation. While quite successful, this approach provides little assurance that “phrases” in the resulting synchronous grammars are semantically coherent units, in the linguistic sense. TTS models obtain better coherency through use of parsers trained on phrase-bracketed text corpora (treebanks). The encoding of English sentences into French (in keeping with our previously assumed language pair) is conceptualized as beginning with a parsed English sentence, which is then transformed by (learned) rules that progressively expand the original or partially transformed pattern of phrases and words until all the leaves are French words.