SlowGuess/NAACL_2022_OCR huggingface dataset:
Table A.1: Spearman's rank correlation of attribution based importance (aggregated by rollout) with saliency scores for the validation set for the BERT-large model fine-tuned on SST-2, MNLI, and HateXplain. The numbers are the average on all the validation set examples (1024 examples for MNLI dataset due to resource limitations)  $\pm$  the standard deviation.
ELECTRA-base	Attention Rollout
SST2	MNLI	HATEXPLAIN
Weight-based (W)	-0.37 ± 0.19	-0.31 ± 0.22	0.02 ± 0.29
w/ Fixed Residual (\( \mathcal{W}_{FIXEDRES} \))	-0.37 ± 0.19	-0.24 ± 0.23	0.01 ± 0.29
w/ Residual (\( \mathcal{W}_{RES} \))	-0.10 ± 0.22	0.08 ± 0.25	0.20 ± 0.27
Norm-based (N)	0.18 ± 0.21	0.12 ± 0.21	0.21 ± 0.26
w/ Fixed Residual (\( \mathcal{N}_{FIXEDRES} \))	0.23 ± 0.22	0.32 ± 0.23	0.28 ± 0.26
w/ Residual (\( \mathcal{N}_{RES} \))	0.54 ± 0.17	0.54 ± 0.14	0.44 ± 0.21
w/ Residual + Layer Norm 1 (\( \mathcal{N}_{RESLN} \))	-0.24 ± 0.23	-0.16 ± 0.24	-0.07 ± 0.28
w/ GlobEnc: [Residual + Layer Norm 1, 2] (\( \mathcal{N}_{ENC} \))	0.64 ± 0.15	0.68 ± 0.12	0.47 ± 0.22
Table A.2: Spearman's rank correlation of attribution based importance (aggregated by rollout) with saliency scores for the validation set for the ELECTRA-base model fine-tuned on SST-2, MNLI, and HateXplain. The numbers are the average on all the validation set examples  $\pm$  the standard deviation.
Figure A.1: Aggregated  $\mathcal{N}_{\mathrm{ENC}}$  attribution maps (GlobEnc) for the [CLS] token for fine-tuned BERT on SST2 dataset (sentiment analysis). These examples were misclassified by the model.
Figure A.2: Aggregated attributions via rollout with different methods across layers. The model is fine-tuned on SST2 dataset and the attention of the CLS token is shown in each layer.
Figure A.3: Aggregated attributions via rollout with different methods in layer 12. The model is fine-tuned on SST2 dataset. Each row indicates how much other tokens impact the token written on the row.
Figure A.4: Aggregated  $\mathcal{N}_{\mathrm{ENC}}$  attribution maps (GlobEnc) for the [CLS] token for fine-tuned BERT on MNLI dataset.
Figure A.5: Aggregated  $\mathcal{N}_{\mathrm{ENC}}$  attribution maps (GlobEnc) for the [CLS] token for fine-tuned ELECTRA on MNLI dataset.
Figure A.6: Aggregated  $\mathcal{N}_{\mathrm{ENC}}$  attribution maps (GlobEnc) for the [CLS] token for fine-tuned BERT-large on MNLI dataset.