Transition-Based Syntactic Linearization

Yijia Liu, Yue Zhang, Wanxiang Che, Bing Qin

Abstract: Syntactic linearization algorithms take a bag of input words and a set of optional constraints, and construct an output sentence and its syntactic derivation simultaneously. The search problem is NP-hard, and the current best results are achieved by bottom-up bestfirst search. One drawback of the method is low efficiency; and there is no theoretical guarantee that a full sentence can be found within bounded time. We propose an alternative algorithm that constructs output structures from left to right using beam-search. The algorithm is based on incremental parsing algorithms. We extend the transition system so that word ordering is performed in addition to syntactic parsing, resulting in a linearization system that runs in guaranteed quadratic time. In standard evaluations, our system runs an order of magnitude faster than a state-of-the-art baseline using best-first search, with improved accuracies.