We propose a method for unsupervised parsing based on the linguistic notion
of a constituency test. One type of constituency test involves modifying the
sentence via some transformation (e.g. replacing the span with a pronoun) and
then judging the result (e.g. checking if it is grammatical). Motivated by this
idea, we design an unsupervised parser by specifying a set of transformations
and using an unsupervised neural acceptability model to make grammaticality
decisions. To produce a tree given a sentence, we score each span by
aggregating its constituency test judgments, and we choose the binary tree with
the highest total score. While this approach already achieves performance in
the range of current methods, we further improve accuracy by fine-tuning the
grammaticality model through a refinement procedure, where we alternate between
improving the estimated trees and improving the grammaticality model. The
refined model achieves 62.8 F1 on the Penn Treebank test set, an absolute
improvement of 7.6 points over the previous best published result.

本文提出了一种基于无监督成分测试的无监督解析方法。通过使用一组转换和一个无监督的神经接受模型来制定无监督解析器，以生成给定句子的树，通过聚合其成分测试判断来评分。并且通过反复改进预测出的树和提高语法性模型之间的相互作用，进一步提高了准确性。