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Improving edit-based unsupervised sentence simplification using fine-tuned BERT
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DOI：10.1016/j.patrec.2023.01.009.png)
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Abstract
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Word suggestion in unsupervised sentence simplification aims to replace complex words of a given sen-tence with their simpler alternatives. This is mostly done without considering their context within the input sentence. In this paper, we propose a technique that brings context awareness to word suggestion by merging pre-trained BERT models with a successful edit-based unsupervised sentence simplification model. More importantly, we show that only by fine-tuning the BERT model on simple English corpora, simplification results can be improved and even outperform some of the competing supervised methods. Finally, we introduce a framework that involves filtering an arbitrary amount of unlabeled in-domain text for tuning the model in situations where labeled data, as simple and complex, is scarce. This preprocess-ing step also speeds up the training process by ignoring fine-tuning on unnecessary samples.(c) 2023 Elsevier B.V. All rights reserved.
Keywords:
Unsupervised sentence simplification
Masked language modeling
Domain adaptation
BERT
Data selection
Natural language processing
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