LSBert: A Simple Framework for Lexical Simplification
TABLE I: Evaluation results of substitute generation on three datasets.
['[EMPTY]', 'LexMTurk PRE', 'LexMTurk RE', 'LexMTurk F1', 'BenchLS PRE', 'BenchLS RE', 'BenchLS F1', 'NNSeval PRE', 'NNSeval RE', 'NNSeval F1']
As can be seen, our model LSBert obtains the highest Recall and F1 scores on three datasets, largely outperforming the previous best baseline Paetzold-NE, increasing 37.4%, 19.1% and 41.4% using F1 metric. The baseline Paetzold-NE by combining the Newsela parallel corpus and context-aware word embeddings obtains better results on PRE than LSBert, because it uses a different calculation method. If one candidate exists in the gold standard, different morphological derivations of the candidate in substitute candidates are all counted into the PRE metric. Because of considering the context, the substitute candidates of Bert based methods are normally different words.