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- W4385569712 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." @default.
- W4385569712 created "2023-08-05" @default.
- W4385569712 creator A5012041701 @default.
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- W4385569712 date "2023-01-01" @default.
- W4385569712 modified "2023-09-24" @default.
- W4385569712 title "Contextual Distortion Reveals Constituency: Masked Language Models are Implicit Parsers" @default.
- W4385569712 doi "https://doi.org/10.18653/v1/2023.acl-long.285" @default.
- W4385569712 hasPublicationYear "2023" @default.
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