SlowGuess/NAACL_2022_OCR huggingface dataset:
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We build both the generator and the siamese semantic-preserving discriminator on the basis of Transformer. For the generator, in both the encoder and the decoder, 6 layers are stacked with dimensions of embedding layers and hidden layers set to 512. The dimension of feed-forward layers is set to 2048. And we set 8 heads for multi-head attention. In the discriminator, both the text encoder and the summary encoder have the same framework as the encoder in the generator, except that the number of layers is set to 2. For the generator, we adopt the joined source-target vocabulary for both English and Chinese experiments. The encoder input embeddings, the decoder input embeddings and the decoder output embeddings are all shared. For the discriminator, the two encoders share the input embeddings.
For the Chinese dataset, we tokenize the sequences into character-level text-summary pairs and evaluate the performance based on the reference tokens. For the English dataset, to improve the computational efficiency and avoid problems with closed vocabularies, we segment the data using byte-pair encoding (BPE) (Sennrich et al., 2016), which results in a vocabulary of 32K tokens.
During pre-training, for the generator, Adam optimizer is used with the learning rate set as 0.0005.
The inverse square root learning rate decay is applied for initial warm up and annealing with 4000 steps. For the discriminator, we adopt RMSProp optimizer with the learning rate of 0.0005 and  $\eta = 0.7$ . The dropout rate is set to 0.3 for both models. During adversarial training, for both models, the learning rate is set to 0.00001 without changing the optimizer.  $K$  in Monte Carlo rollout is set as 20 and  $\lambda$  is 0.7.
In the proposed architecture, there are 2 hyperparameters  $\eta$  and  $\lambda$  need to be jointly tuned during training. Here we conduct a grid search to find a proper combination of these hyper-parameters. For both  $\eta$  and  $\lambda$ , the value is selected in set [0.1, 0.3, 0.5, 0.7, 0.9] and we experimentally find that the  $\eta$  of 0.7 and the  $\lambda$  of 0.7 give the best results on validation sets.
A.2 Pseudo Code