UNBNLP at SemEval-2021 Task 1: Predicting lexical complexity with masked language models and character-level encoders
Milton King, Ali Hakimi Parizi, Samin Fakharian, Paul F. Cook · 2021
In this paper, we present three supervised systems for English lexical complexity prediction of single and multiword expressions for SemEval-2021 Task 1.We explore the use of statistical baseline features, masked language models, and character-level encoders to predict the complexity of a target token in context.Our best system combines information from these three sources.The results indicate that information from masked language models and character-level encoders can be combined to improve lexical complexity prediction.