LSBert: A Simple Framework for Lexical Simplification
TABLE II: The evaluation results using Precision (PRE) and Accuracy (ACC) on three datasets.
['[EMPTY]', 'LexMTurk PRE', 'LexMTurk ACC', 'BenchLS PRE', 'BenchLS ACC', 'NNSeval PRE', 'NNSeval ACC']
We can see that our method LSBert attains the highest Accuracy on three datasets, which has an average increase of 29.8% over the former state-of-the-art baseline (Paetzold-NE). It suggests that LSBert is the most proficient in promoting simplicity. Paetzold-NE obtains higher than LSBert on Precision on NNSeval, which also means that many complex words are replaced by the original word itself, due to the shortage of simplification rules in parallel corpora. REC-LS obtains the best PRE and poor ACC, because it prefers the original word as the substitute word.