Contextual Distortion Reveals Constituency: Masked Language Models are Implicit Parsers

• URL: http://arxiv.org/abs/2306.00645v1
• Date: Thu, 1 Jun 2023 13:10:48 GMT
• Title: Contextual Distortion Reveals Constituency: Masked Language Models are Implicit Parsers
• Authors: Jiaxi Li and Wei Lu
• Abstract summary: We propose a novel method for extracting parse trees from masked language models (LMs) Our method computes a score for each span based on the distortion of contextual representations resulting from linguistic perturbations. Our method consistently outperforms previous state-of-the-art methods on English with masked LMs, and also demonstrates superior performance in a multilingual setting.
• Score: 7.558415495951758
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advancements in pre-trained language models (PLMs) have demonstrated that these models possess some degree of syntactic awareness. To leverage this knowledge, we propose a novel chart-based method for extracting parse trees from masked language models (LMs) without the need to train separate parsers. Our method computes a score for each span based on the distortion of contextual representations resulting from linguistic perturbations. We design a set of perturbations motivated by the linguistic concept of constituency tests, and use these to score each span by aggregating the distortion scores. To produce a parse tree, we use chart parsing to find the tree with the minimum score. Our method consistently outperforms previous state-of-the-art methods on English with masked LMs, and also demonstrates superior performance in a multilingual setting, outperforming the state of the art in 6 out of 8 languages. Notably, although our method does not involve parameter updates or extensive hyperparameter search, its performance can even surpass some unsupervised parsing methods that require fine-tuning. Our analysis highlights that the distortion of contextual representation resulting from syntactic perturbation can serve as an effective indicator of constituency across languages.

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