We use large-scale auto-parsed data to obtain subtrees on the target side.

Then we generate the mapping rules to map the source subtrees onto the extracted target subtrees .

These features indicate the information of the constraints between bilingual subtrees , that are called bilingual subtree constraints.

We design bilingual subtree features, as described in Section 4, based on the constraints between the source subtrees and the target subtrees that are verified by the subtree list on the target side.

The source subtrees are from the possible dependency relations.

The subtrees are extracted from large-scale auto-parsed monolingual data on the target side.

Adaptor grammars are an example of this approach (Johnson et al., 2007b), where entire subtrees generated by a “base grammar” can be viewed as distinct rules (in that we learn a separate probability for each subtree).

The inference task is nonparametric if there are an unbounded number of such subtrees .

(Word s i) (Word d 6) (Word b u k) Because the Word nonterminal is adapted (indicated here by underlining) the adaptor grammar learns the probability of the entire Word subtrees (e.g., the probability that b a k is a Word); see Johnson (2008) for further details.

We use “term” to refer to text expressions, and “components” to refer to nodes, edges, and subtrees .

Figure 1: The Substitution transformation, demonstrated on the relevant subtrees of Example (i).

For each bridging relation, it adds a specific subtrees 87" via an edge labeled with labr.