Guided Dialog Policy Learning: Reward Estimation for Multi-Domain Task-Oriented Dialog
Table 5: Performance of different agents on the neural user simulator.
['Method', 'VHUS Turns', 'VHUS Inform', 'VHUS Match', 'VHUS Success']
the agent The performance that interacts with VHUS is presented in Table 5. [CONTINUE] All the methods cause a significant drop in performance when interacting with VHUS. ALDM even gets worse performance than ACER and PPO. In comparison, GDPL is still comparable with ACER and PPO, obtains a better match rate, and even achieves higher task success.