LSBert: A Simple Framework for Lexical Simplification
TABLE IV: Ablation study results of the ranking features.
['[EMPTY]', 'LexMTurk PRE', 'LexMTurk ACC', 'BenchLS PRE', 'BenchLS ACC', 'NNSeval PRE', 'NNSeval ACC']
To determine the importance of each ranking feature, we make an ablation study by removing one feature in turn. We can see that LSBert combining all five features achieves the best results, which means all features have a positive effect. LSBert removing frequency feature achieves better results on PRE metric, but it decreases the values of ACC. These features have different contributions for LSBert’s performance, for example, PPDB feature brings the least impact on the performance of LSBert compared with the other features. In this paper, LSBert thinks all features are equally important, that may not be the best option. In the future, we can improve LSBert by combining these features using different weights.