Selection Bias Explorations and Debias Methods forNatural Language Sentence Matching Datasets
Table 2: Ablation experiments of the three leakage features on the datasets. “┐” means without the feature. We report the accuracy scores and “%” is omitted.
['[BOLD] Features', '[BOLD] SNLI', '[BOLD] QuoraQP', '[BOLD] SICKSTS', '[BOLD] ByteDance']
We conduct ablation experiments on the datasets where the leakage features are effective, i.e., SNLI, QuoraQP, SICKSTS and ByteDance. We can see that S2_freq is more effective in SNLI, and S1_freq plays a more critical role in SICKSTS, while in QuoraQP and ByteDance, S1S2_inter is the most predictive.