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

Unsupervised Parsing via Constituency Tests