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Unsupervised statistical text simplification using pre-trained language modeling for initialization

Published: 08 August 2022 Publication History

Abstract

Unsupervised text simplification has attracted much attention due to the scarcity of high-quality parallel text simplification corpora. Recent an unsupervised statistical text simplification based on phrase-based machine translation system (UnsupPBMT) achieved good performance, which initializes the phrase tables using the similar words obtained by word embedding modeling. Since word embedding modeling only considers the relevance between words, the phrase table in UnsupPBMT contains a lot of dissimilar words. In this paper, we propose an unsupervised statistical text simplification using pre-trained language modeling BERT for initialization. Specifically, we use BERT as a general linguistic knowledge base for predicting similar words. Experimental results show that our method outperforms the state-of-the-art unsupervised text simplification methods on three benchmarks, even outperforms some supervised baselines.

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Published In

cover image Frontiers of Computer Science: Selected Publications from Chinese Universities
Frontiers of Computer Science: Selected Publications from Chinese Universities  Volume 17, Issue 1
Feb 2023
231 pages
ISSN:2095-2228
EISSN:2095-2236
Issue’s Table of Contents

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 08 August 2022
Accepted: 06 September 2021
Received: 10 May 2021

Author Tags

  1. text simplification
  2. pre-trained language modeling
  3. BERT
  4. word embeddings

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  • (2024)MPGrafProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/937(8439-8443)Online publication date: 3-Aug-2024
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  • (2023)Transfer Learning-Based Neural Machine Translation for Low-Resource LanguagesACM Transactions on Asian and Low-Resource Language Information Processing10.1145/3618111Online publication date: 13-Sep-2023

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