A Simple BERT-Based Approach for Lexical Simplification
Table 1: Evaluation results of simplification candidate generation on three datasets.
['[EMPTY]', 'LexMTurk Precision', 'LexMTurk Recall', 'LexMTurk F1', 'BenchLS Precision', 'BenchLS Recall', 'BenchLS F1', 'NNSeval Precision', 'NNSeval Recall', 'NNSeval F1']
As can be seen, despite being entirely unsupervised, our model BERT-LS obtains F1 scores on three datasets, largely outperforming the previous best baselines. The baseline Paetzold-NE by combining the Newsela parallel corpus and context-aware word embeddings obtains better results on Precision metric, which demonstrates that substitution generation tends to benefit from the combination of different resources. But, for under-resourced languages, our method is still likely to create competitive candidate generators. The results clearly show that BERT-LS provides a good balance precision and recall using only BERT over raw text.