Guided Dialog Policy Learning: Reward Estimation for Multi-Domain Task-Oriented Dialog
Table 4: KL-divergence between different dialog policy and the human dialog KL(πturns||pturns), where πturns denotes the discrete distribution over the number of dialog turns of simulated sessions between the policy π and the agenda-based user simulator, and pturns for the real human-human dialog.
['GP-MBCM', 'ACER', 'PPO', 'ALDM', 'GDPL']
Table 4 shows that GDPL has the smallest KL-divergence to the human on the number of dialog turns over the baselines, which implies that GDPL behaves more like the human.