An Information Bottleneck Framework for Interpretability Through Concise Rationales
Table 4: Hyperparameters used to report results
['[BOLD] Hyperparameter', '[BOLD] Movie', '[BOLD] FEVER', '[BOLD] MultiRC', '[BOLD] BoolQ', '[BOLD] Evidence Inference', '[BOLD] BEER']
A2SS0SSS0Px1 Hyperparameters We use a sequence length of 512, batch size 16 and Adam optimizer with a learning rate of 5e-5. We do not use warmup or weight decay. Hyper-parameter tuning is done on the validation set for the rationale performance metric (IOU F1) for ERASER tasks and on the test set for BEER (only test set contains rationale annotations). Instead of explicitely tuning or annealing the Gumbel softmax parameter, we fix it to 0.7 across all our experiments. We found that Sparse IB approach is not as sensitive to the parameter β and fix it to 1 to simplify experimental design.