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Unsupervised statistical text simplification using pre-trained language modeling for initialization |
Jipeng QIANG1( ), Feng ZHANG1, Yun LI1(), Yunhao YUAN1, Yi ZHU1, Xindong WU2,3 |
1. Department of Computer Science, Yangzhou University, Yangzhou 225127, China
2. Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology), Ministry of Education, Hefei 23009, China
3. Mininglamp Academy of Sciences, Mininglamp, Beijing 100089, China |
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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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| Keywords
text simplification
pre-trained language modeling
BERT
word embeddings
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Corresponding Author(s):
Jipeng QIANG,Yun LI
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Just Accepted Date: 09 September 2021
Issue Date: 01 March 2022
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