SlowGuess/ACL_2020_OCR huggingface dataset:
Model	Accuracy	MAE
KT	EM	20Y	50Y
A	BoE	0.674 ± 0.010	7.9 ± 0.7	49.6 ± 0.6	63.1 ± 0.4	48.2 ± 1.8
– event text	0.631 ± 0.009	7.0 ± 0.6	48.2 ± 0.7	60.6 ± 0.7	54.6 ± 1.1
LSTM	0.765 ± 0.009	1.8 ± 0.4	50.2 ± 4.6	78.8 ± 1.9	39.0 ± 2.1
– event text	0.694 ± 0.011	1.3 ± 0.4	39.9 ± 5.1	69.7 ± 4.3	50.6 ± 3.3
B	BoE	0.668 ± 0.007	9.1 ± 0.4	55.3 ± 0.8	71.0 ± 0.7	50.7 ± 1.2
– years	0.623 ± 0.007	7.4 ± 0.3	46.1 ± 0.7	65.0 ± 0.2	61.1 ± 0.7
– dates	0.637 ± 0.008	7.4 ± 0.5	46.7 ± 0.5	64.2 ± 0.8	60.5 ± 1.6
– numbers	0.614 ± 0.007	8.0 ± 0.3	46.3 ± 0.7	64.2 ± 1.0	63.1 ± 0.7
LSTM	0.774 ± 0.006	1.8 ± 0.3	50.4 ± 3.6	77.3 ± 1.4	39.9 ± 1.6
– years	0.733 ± 0.007	1.3 ± 0.5	41.0 ± 1.2	68.5 ± 0.9	48.3 ± 1.4
– dates	0.732 ± 0.004	1.4 ± 0.2	42.5 ± 0.6	70.1 ± 1.0	47.7 ± 0.6
– numbers	0.723 ± 0.007	1.3 ± 0.3	40.1 ± 1.2	68.8 ± 0.7	49.1 ± 1.0
Table 5: Ablation study for BoE and LSTM models. We include a  $95\%$  confidence interval of the mean of each metric computed over 6 runs. Study A was conducted only on datapoints from the WOTD dataset with contextual information, and we report the impact of removing the event text from both models. Study B was conducted on all datapoints from the WOTD dataset, and we report the impact of removing tokens denoting years, dates and numbers. Tokens of these types were removed from the event text and from the contextual information.  $\mathrm{KT} =$  Kendall's Tau,  $\mathrm{EM} =$  Exact Match,  $20\mathrm{Y} =$  Distance under  $20\mathrm{Y}$ ,  $50\mathrm{Y} =$  Distance under  $50\mathrm{Y}$ ,  $\mathrm{MAE} =$  Mean Absolute Error.
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