摘要
1 Introduction Lexical simplification(LS)aims to simplify a sentence by replacing complex words with simpler words without changing the meaning of the sentence,which can facilitate comprehension of the text for people with non-native speakers and children.Traditional LS methods utilize linguistic databases(e.g.,WordNet)[1]or word embedding models[2]to extract synonyms or high-similar words for the complex word,and then sort them based on their appropriateness in context.Recently,BERT-based LS methods[3,4]entirely or partially mask the complex word of the original sentence,and then feed the sentence into pretrained modeling BERT[5]to obtain the top probability tokens corresponding to the masked word as the substitute candidates.They have made remarkable progress in generating substitutes by making full use of the context information of complex words,that can effectively alleviate the shortcomings of traditional methods.
基金
supported by the National Natural Science Foundation of China(Grant Nos.62076217 and 61906060)
the Blue Project of Yangzhou University.
