We propose an alternative approach to generating forests that is based on combining sub-trees within the first best parse through binarization .

Provably, our binarization forest can cover any non-consitituent phrases in a sentence but maintains the desirable property that for each span there is at most one nonterminal so that the grammar constant for decoding is relatively small.

For the purpose of reducing search errors, we apply the synchronous binarization technique to forest-to-string decoding.

To focus on structural variants, we propose a family of binarization algorithms to expand one single constituent tree into a packed forest of binary trees containing combinations of adj acent tree nodes.

0 Forests are not generated by a parser but by combining substructures using a tree binarizer .

For the first time, we show that similar to string-to-tree decoding, synchronous binarization significantly reduces search errors and improves translation quality for forest-to-string decoding.

The motivation of tree binarization is to factorize large and rare structures into smaller but frequent ones to improve generalization.

If long sequences are binarized,

In particular, we integrate synchronous binarizations , verb regrouping, removal of redundant parse nodes, and incorporate a few important features such as translation boundaries.

E T as above-mentioned, such as binarizations , at different levels for constructing partial hypothesis.

We specified a few operators for transforming an elementary tree 7, including flattening tree operators such as removing interior nodes in vi, or grouping the children via binarizations .

Obvious systematic linguistic divergences between language-pairs could be handled by some simple operators such as using binarization to regroup contiguously aligned children.

3.4.1 Binarizations

Whenever it is possible, binarization of LCFRS rules, or reduction of rank to two, is therefore important for parsing, as it reduces the time complexity needed for dynamic programming.

This has lead to a number of binarization algorithms for LCFRSs, as well as factorization algorithms that factor rules into new rules with smaller rank, without necessarily reducing rank all the way to two.

Kuhlmann and Satta (2009) present an algorithm for binarizing certain LCFRS rules without increasing their fanout, and Sagot and Satta (2010) show how to reduce rank to the lowest value possible for LCFRS rules of fanout two, again without increasing fanout.