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A series of recent papers has used a parsing algorithm due to Shen et al.(2018) to recover phrase-structure trees based on proxies for "syntacticdepth." These proxy depths are obtained from the representations learned byrecurrent language models augmented with mechanisms that encourage the(unsupervised) discovery of hierarchical structure latent in natural languagesentences. Using the same parser, we show that proxies derived from aconventional LSTM language model produce trees comparably well to thespecialized architectures used in previous work. However, we also provide adetailed analysis of the parsing algorithm, showing (1) that it isincomplete---that is, it can recover only a fraction of possible trees---and(2) that it has a marked bias for right-branching structures which results ininflated performance in right-branching languages like English. Our analysisshows that evaluating with biased parsing algorithms can inflate the apparentstructural competence of language models.

A Critical Analysis of Biased Parsers in Unsupervised Parsing