It has been shown that transition-based methods can be used for syntactic word ordering and tree linearization, achieving significantly faster speed compared with traditional best-first methods. State-of-the-art transition-based models give competitive results on abstract word ordering and unlabeled tree linearization, but significantly worse results on labeled tree linearization. We demonstrate that the main cause for the performance bottleneck is the sparsity of Shift transition actions rather than heavy pruning. To address this issue, we propose a modification to the standard transition-based feature structure, which reduces feature sparsity and allows lookahead features at a small cost to decoding efficiency. Our model gives the best reported accuracies on all benchmarks, yet still being over 30 times faster compared with best-first-search.
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