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All models are implemented in PyTorch. We use 300-dimensional GloVe embeddings while training on TimeBank and 100-dimensional Word2Vec embeddings trained on Project Gutenberg texts (similar to (Sims et al., 2019)) while training on LitBank. Both source and target domains share a common vocabulary and embedding layer which is not finetuned during the training process. All LSTM models use a hidden size of 100, with an input dropout of 0.5. The POS model uses 50-dimensional embeddings for POS tags which are randomly initialized and finetuned during training. The BERT model uses the uncased variant of BERT-Base for feature extraction. We generate token representations by running BERT-Base and concatenating the outputs of the model's last 4 hidden layers. The event classifier is a single-layer 100-dimensional MLP. For the adversarial training setup, we experiment with values from [0.1,0.2,0.5,1.0,2.0,5.0] for the hyperparameter  $\lambda$ . The domain predictor (adversary) is a 3-layer MLP with each layer having a dimensionality of 100 and ReLU activations between layers. We train all models with a batch size of 16 and use the Adam optimizer with default learning rate settings. Models are trained for 1000 epochs, with early stopping. For finetuning experiments, we train for 10 epochs.
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