Conversational Question Reformulation via Sequence-to-Sequence Architectures and Pretrained Language Models
Table 2: Model settings.
['[EMPTY]', '# parameters', 'Learning rate', 'Batch size']
We train each model on the CANARD training set and select the checkpoint with the best performance on development set. In addition to comparing model performance on the CANARD test set, we directly use the model trained on CANARD to perform CQR on the CAsT dataset.