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Inducing Syntactic Trees from Bert Representations (arxiv.org)
3 points by sel1 on Jun 29, 2019 | hide | past | pdf | discuss on HN

In plain words: Delete one word at a time and watch how much the language model's picture of the rest shifts; removable words like adjectives barely move it, while a main verb shakes it up. These shift scores track grammatical roles and build full word-link trees.

Abstract · Inducing Syntactic Trees from BERT Representations

We use the English model of BERT and explore how a deletion of one word in a sentence changes representations of other words. Our hypothesis is that removing a reducible word (e.g. an adjective) does not affect the representation of other words so much as removing e.g. the main verb, which makes the sentence ungrammatical and of "high surprise" for the language model. We estimate reducibilities of individual words and also of longer continuous phrases (word n-grams), study their syntax-related properties, and then also use them to induce full dependency trees.

Rudolf Rosa, David Mareček
arXiv:1906.11511 · cs.CL · submitted Jun 27, 2019
abstract · pdf · html · Accepted abstract for the BlackboxNLP 2019

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