Adversarial Filters of Dataset Biases
Table 3: Dev accuracy (%) on the original SNLI dataset D and the datasets obtained through different AFLite-filtering and other baselines. D92k indicates a randomly subsampled train dataset of the same size as D(ϕRoBERTa). Δ indicates the difference in performance between the full model and the model trained on D(ϕRoBERTa).
['Model', 'Train Data  [ITALIC] D', 'Train Data  [ITALIC] D92 [ITALIC] k', 'Train Data  [ITALIC] D(Φ [ITALIC] ESIM+GLoVe)', 'Train Data  [ITALIC] D( [ITALIC] ϕBERT)', 'Train Data  [ITALIC] D( [ITALIC] ϕRoBERTa)', 'Δ']
S4SS2SSS0Px1 Snli In all cases, applying AFLite substantially reduces overall model accuracy, with typical drops of 15-35% depending on the models used for learning the feature representations and those used for evaluation of the filtered dataset. In general, performance is lowest when using the strongest model (RoBERTa) for learning feature representations. Results also highlight the ability of weaker adversaries to produce datasets that are still challenging for much stronger models with a drop of 13.7% for RoBERTa using ΦESIM+GLoVe as feature representation. Finally, to demonstrate the value of the iterative, ensemble-based AFLite algorithm, we compare with a baseline where using a single model, we filter out the most predictable examples in a single iteration — a non-iterative, single-model version of AFLite. A RoBERTa-large model trained on this subset (of the same size as D(ϕRoBERTa)) achieves a dev accuracy of 72.1%. Compared to the performance of RoBERTa on D(ϕRoBERTa) In particular, this illustrates the need for an iterative procedure involving models trained on multiple partitions of the remaining data in each iteration. This indicates that the filtered dataset is somewhat harder even for humans, though to a much lesser degree than any model. Future directions for bias reduction techniques should account for unaltered human performance before and after reduction.