Dependency Parsing as Head Selection
Xingxing Zhang, Jianpeng Cheng, Mirella Lapata · 2017
Conventional graph-based dependency parsers guarantee a tree structure both during training and inference.Instead, we formalize dependency parsing as the problem of independently selecting the head of each word in a sentence.Our model which we call DENSE (as shorthand for Dependency Neural Selection) produces a distribution over possible heads for each word using features obtained from a bidirectional recurrent neural network.Without enforcing structural constraints during training, DENSE generates (at inference time) trees for the overwhelming majority of sentences, while non-tree outputs can be adjusted with a maximum spanning tree algorithm.We evaluate DENSE on four languages (English, Chinese, Czech, and German) with varying degrees of non-projectivity.Despite the simplicity of the approach, our parsers are on par with the state of the art. 1