{"title":"PolyU-CBS at TSAR-2022 Shared Task: A Simple, Rank-Based Method for Complex Word Substitution in Two Steps","authors":"Emmanuele Chersoni, Yu-Yin Hsu","doi":"10.18653/v1/2022.tsar-1.24","DOIUrl":null,"url":null,"abstract":"In this paper, we describe the system we presented at the Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022) regarding the shared task on Lexical Simplification for English, Portuguese, and Spanish. We proposed an unsupervised approach in two steps: First, we used a masked language model with word masking for each language to extract possible candidates for the replacement of a difficult word; second, we ranked the candidates according to three different Transformer-based metrics. Finally, we determined our list of candidates based on the lowest average rank across different metrics.","PeriodicalId":247582,"journal":{"name":"Proceedings of the Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18653/v1/2022.tsar-1.24","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
In this paper, we describe the system we presented at the Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022) regarding the shared task on Lexical Simplification for English, Portuguese, and Spanish. We proposed an unsupervised approach in two steps: First, we used a masked language model with word masking for each language to extract possible candidates for the replacement of a difficult word; second, we ranked the candidates according to three different Transformer-based metrics. Finally, we determined our list of candidates based on the lowest average rank across different metrics.