Task: Question Generation
Input (English): We apply the same data split in BIBREF31, BIBREF30, BIBREF6, which uses news (the union of bn and nw) as the training set, a half of bc as the development set and the remaining data as the test set.

We learn the model parameters using Adam BIBREF32. We apply dropout BIBREF33 to the hidden layers to reduce overfitting. The development set is used for tuning the model hyperparameters and for early stopping. We train 5 Bi-LSTM English RE models initiated with different random seeds, apply the 5 models on the target languages, and combine the outputs by selecting the relation type labels with the highest probabilities among the 5 models.
Output (English): Do they train their own RE model?