From Credit Assignment to Entropy Regularization: Two New Algorithms for Neural Sequence Prediction
Table 6: Hyper-parameters for ERAC training
['[BOLD] Hyper-parameters', '[BOLD] MT w/ input feeding', '[BOLD] MT w/o input feeding', '[BOLD] Image Captioning']
A2SS4SSS0Px2 Ac & Erac In all phases, the learning rate is halved when there is no improvement on the validation set. We use the same hyper-parameters for AC training, except that the entropy regularization coefficient τ is 0. Similar to the VAML case, the gradient clipping value is set to 5.0 for both the actor and the critic, except that we set the gradient clipping value to 1.0 for the actor in the captioning task.