Imbalanced Sentiment Classification Enhanced with Discourse Marker
Table 2: Ablation test on attention-based validator. “wo/” represents “without”. “val” denotes “validator”. “full” denotes our full model.
['Method', 'IR', 'Setting', 'MR', 'SST2', 'CR', 'Avg improvement']
We can observe that validator indeed contributes a lot to the total performance. As discussed in the last part of Section 3.1, the generated sample must agree with its label. In our method, the discourse sample may state a simple fact and be sentiment-irrelevant. Our attention-based model serves as a validator and filters out false labeled cases, significantly improving the quality of expanded dataset.