SentiLR: Linguistic Knowledge Enhanced Language Representation for Sentiment Analysis
Table 3: Accuracy on the test sets of SST and MR (%). The size of training sets means the size of the original training sets sampled from the full set. w/ indicates that the training sets are augmented by C-BERT / SentiLR-B.
['Dataset Size of', 'SST 2000', 'SST 5000', 'SST Full', 'MR 2000', 'MR 5000', 'MR Full']
Thus, we tested the augmentation ability of our model on two sentence-level classification datasets, i.e., SST and MR. Specifically, we sampled some subsets at different scales from the original dataset as our training sets. For each sentence in the training sets, we constructed 4 augmented samples, which were then added to the training data. The detailed augmentation settings are described in the Appendix. The results also show that SentiLR-B can acquire augmented data with more inductive biases than C-BERT. In particular, for the RNN classifier, the performance boost with training data augmented by SentiLR-B is more significant. It demonstrates that our model can generate more plausible augmented samples with enhanced representations that capture the dependency within contexts and the relationship between labels and words.