{Andrew McCallum Mohit Iyyer Tim O{'}Gorman Yi-Pei Chen Subendhu Rongali Andrew Drozdov}

Abstract

The deep inside-outside recursive autoencoder (DIORA; Drozdov et al. 2019) is a self-supervised neural model that learns to induce syntactic tree structures for input sentences without access to labeled training data. In this paper, we discover that while DIORA exhaustively encodes all possible binary trees of a sentence with a soft dynamic program, its vector averaging approach is locally greedy and cannot recover from errors when computing the highest scoring parse tree in bottom-up chart parsing. To fix this issue, we introduce S-DIORA, an improved variant of DIORA that encodes a single tree rather than a softly-weighted mixture of trees by employing a hard argmax operation and a beam at each cell in the chart. Our experiments show that through fine-tuning a pre-trained DIORA with our new algorithm, we improve the state of the art in unsupervised constituency parsing on the English WSJ Penn Treebank by 2.2-6{%} F1, depending on the data used for fine-tuning.