A Dive into Lexical Simplification with Pre-trained Model
Pages 92 - 95
Abstract
Lexical Simplification (LS) targets the replacement of complex terms with semantically equivalent simpler versions. While traditional LS methods often produce imprecise candidates, contemporary techniques utilize BERT for context-aware replacements. Notably, simplification may require phrasal rather than word-to-word substitutions. Diverging from conventional methods, our approach enables phrase-based replacements, and We improved the masking method of the masked language model to make it more suitable for lexical simplification tasks, finally refined the candidate word ranking. Experimental results show our method exceeds standard benchmarks.
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- A Dive into Lexical Simplification with Pre-trained Model
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Published In
October 2023
1065 pages
ISBN:9798400709449
DOI:10.1145/3650215
Copyright © 2023 ACM.
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Published: 16 April 2024
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ICMLCA 2023
ICMLCA 2023: 2023 4th International Conference on Machine Learning and Computer Application
October 27 - 29, 2023
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