SlowGuess/NAACL_2021_OCR huggingface dataset:
Hyper-parameters	Search space
Learning rate lr	{0.001, 0.0001, 0.00001}
Embedding dimension d	{30, 64, 128, 300}
Batch size b	{256, 512, 1024, 2048, 4096}
Gumbel box temperature β	{0.1, 0.01, 0.001, 0.0001}
L2 regularization λ	{0.001, 0.01, 0.1, 1}
The best hyper-parameter combinations for confidence prediction are  $\{lr = 0.0001, b = 1024, d = 64, \beta = 0.01\}$ ,  $b = 2048$  for CN15k and  $b = 4096$  for NL27k.  $L_{2}$  regularization is 1 for box sizes in logarithm scale and 0.001 for other parameters. For fact ranking they are  $\{lr = 0.0001, d = 300, b = 4096, \lambda = 0.00001\}$ ,  $\beta = 0.001$  for CN15k and  $\beta = 0.0001$  for NL27k. The number of negative samples is fixed as 30. Rule weights are empirically set as  $w_{tr} = w_{cp} = 0.1$ .
Table 7 lists the hardware specifications of the machine where we train and evaluate all models. On this machine, training BEUrRE for the confidence prediction task takes around 1-1.5 hours. Training BEUrRE for the ranking task takes around 1-2 hours for CN15k and 3 hours for NL27k. For the reported model, on CN15k, BEUrRE has around 2M parameters for confidence prediction and 9M parameters for ranking. On NL27k, BEUrRE has 9M parameters for confidence prediction and 17M for ranking.
Table 6: Search Space for hyper-parameters
Hardware	Specification
CPU	Intel® Xeon® E5-2650 v4 12-core
GPU	NVIDIA® GP102 TITAN Xp (12GB)
RAM	256GB
Table 7: Hardware specifications of the used machine