Self-Balanced Dropout
Table 1: Effectiveness of Self-Balanced Dropout on sentence classification task. p1 and p2 are the keep probability (1 - dropout rate) of the input layer and the hidden layer respectively. The first line is the result of CNN-non-static model in Kim (2014). Results also include: MGNC-CNN Zhang et al. (2016b), MVCNN Yin and Schütze (2016), DSCNN Zhang et al. (2016a), Semantic-CNN Li et al. (2017) and TopCNNword Zhao and Mao (2017).
['Model', 'MR', 'SST-1', 'SST-2', 'Subj', 'TREC', 'CR', 'MPQA']
The results of Self-Balanced Dropout are listed in “CNN-SB-Dropout” row. The revised dropout further improves accuracy on all seven datasets. We also list the results of other models which either use multiple pre-trained embedding as inputs or use more complex deep models. By adding xmask into the original dropout, Self-Balanced Dropout can also achieve competitive performance against these more sophisticated methods.