A Simple BERT-Based Approach for Lexical Simplification
Table 3: Full pipeline evaluation results using Precision and Accuracy on three datasets.
['[EMPTY]', 'LexMTurk Precision', 'LexMTurk Accuracy', 'BenchLS Precision', 'BenchLS Accuracy', 'NNSeval Precision', 'NNSeval Accuracy']
We can see that our method BERT-LS attains the highest Accuracy on three datasets, which has an average increase of 12% over the former state-of-the-art baseline (Paetzold-NE). It suggests that BERT-LS is the most proficient in promoting simplicity. Paetzold-NE obtains higher than BERT-LS on Precision on BenchLS and NNSeval, which means that many complex words in two datasets are replaced by the original word itself, due to the shortage of simplification rules in parallel corpora. In conclusion, although BERT-LS only uses raw text for pre-trained BERT without using any resources, BERT-LS remains the best lexical simplification method. The results are in accordance with the conclusions of the Simplification Candidate Generation.