Lexical Simplification with Neural Ranking
Gustavo Henrique Paetzold, Lucia Specia · 2017
We present a new Lexical Simplification approach that exploits Neural Networks to learn substitutions from the Newsela corpus -a large set of professionally produced simplifications.We extract candidate substitutions by combining the Newsela corpus with a retrofitted context-aware word embeddings model and rank them using a new neural regression model that learns rankings from annotated data.This strategy leads to the highest Accuracy, Precision and F1 scores to date in standard datasets for the task.