Lexical Simplification with Neural Ranking

EACL 2017  ·  Gustavo Paetzold, Lucia Specia ·

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. read more

PDF Abstract


Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.


No methods listed for this paper. Add relevant methods here