DeepAI AI Chat
Log In Sign Up

Inducing Syntactic Trees from BERT Representations

06/27/2019
by   Rudolf Rosa, et al.
Charles University in Prague
0

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.

READ FULL TEXT
09/23/2021

Putting Words in BERT's Mouth: Navigating Contextualized Vector Spaces with Pseudowords

We present a method for exploring regions around individual points in a ...
07/21/2020

Word Representation for Rhythms

This paper proposes a word representation strategy for rhythm patterns. ...
11/19/2015

Good, Better, Best: Choosing Word Embedding Context

We propose two methods of learning vector representations of words and p...
06/20/2022

SynWMD: Syntax-aware Word Mover's Distance for Sentence Similarity Evaluation

Word Mover's Distance (WMD) computes the distance between words and mode...
10/16/2013

Distributed Representations of Words and Phrases and their Compositionality

The recently introduced continuous Skip-gram model is an efficient metho...
04/26/2022

Pretraining Chinese BERT for Detecting Word Insertion and Deletion Errors

Chinese BERT models achieve remarkable progress in dealing with grammati...