Chengxue Yang
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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Index Terms
- A Dive into Lexical Simplification with Pre-trained Model
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