SlowGuess/NAACL_2022_OCR huggingface dataset:
The model is trained with cross entropy loss using an SGD optimizer with momentum. Early stopping is used on the F1 score of the validation set, with a patience of 10 epochs. During training, negative sentences in the training set are downsampled to  $90\%$  (resampled every epoch) instead of  $97\%$ , which leads to 3x faster training time but minimal impact on performance. Validation test sets are used in their entirety. Training hy parameters are shown in Table 8. Training t cally takes less than 2 hours to complete on a sir GeForce RTX 2080 Ti GPU.
Hyperparameter	Value
Batch size	64
Learning rate	0.02
SGD momentum	0.9
Early stopping patience	10
Table 8: Training hyperparameters.
C Confusion Matrices
Figure 7 shows the confusion matrices for the six classification experiments. As mentioned in main text, an in-context classification accuracy can be calculated from the tokens that are correctly identified as part of an EE but may or may not have a correct prediction of the orders (i.e. confuse with B-fake). For example, the in-context classification accuracy for the CNN confusion matrix
$$
a c c _ {C N N} = \frac {439 + 447}{439 + 447 + 4} = 99.55 \%
$$
Figure 7: Confusion matrices for the swap classification experiments for LSTM (top) and CNN (bottom)