Guided Dialog Policy Learning: Reward Estimation for Multi-Domain Task-Oriented Dialog
Table 7: Return distribution of GDPL on each metric. The first row counts the dialog sessions that get the full score of the corresponding metric, and the results of the rest sessions are included in the second row.
['Type', 'Inform Mean', 'Inform Num', 'Match Mean', 'Match Num', 'Success Mean', 'Success Num']
Table 7 provides a quantitative evaluation on the learned rewards by showing the distribution of the return R = t γtrt according to each metric. [CONTINUE] It can be observed that the learned reward function has good interpretability in that the reward is positive when the dialog gets a full score on each metric, and negative otherwise.