Reinforcement Learning based Curriculum Optimization forNeural Machine Translation
Table 2: BLEU scores on ablation experiments with fixed rewards or observations.
['Reward/Observations', 'Default', 'Fixed']
Task-specific reward and observation engineering is critical when building an RL model. We performed ablation experiments to determine if the rewards and observations we have chosen contain information which aids us in the curriculum learning task. The fixed reward experiments were conducted by replacing the default delta-perplexity based reward with a static reward which returns a reward of one when the cleanest bin was selected and zero otherwise. The fixed observation experiments used a static vector of ones as input at all time steps. Both settings perform worse than our default setup, indicating that both the chosen observation and reward provide information to help with this task.