Transition-based syntactic linearization

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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.

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Liu, Y., Zhang, Y., Che, W., & Qin, B. (2015). Transition-based syntactic linearization. In NAACL HLT 2015 - 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference (pp. 113–122). Association for Computational Linguistics (ACL). https://doi.org/10.3115/v1/n15-1012

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