Guided Dialog Policy Learning: Reward Estimation for Multi-Domain Task-Oriented Dialog
Table 3: Performance of different dialog agents on the multi-domain dialog corpus by interacting with the agenda-based user simulator. All the results except “dialog turns” are shown in percentage terms. Real human-human performance computed from the test set (i.e. the last row) serves as the upper bounds.
['Method', 'Agenda Turns', 'Agenda Inform', 'Agenda Match', 'Agenda Success']
The performance of each approach that interacts with the agenda-based user simulator is shown in [CONTINUE] Table 3. GDPL achieves extremely high performance in the task success on account of the substantial improvement in inform F1 and match rate over the baselines. [CONTINUE] Surprisingly, GDPL even outperforms human in completing the task, and its average dialog turns are close to those of humans, though GDPL is inferior in terms of match rate. [CONTINUE] ACER and PPO obtain high performance in inform F1 and match rate as well. [CONTINUE] Though ALDM obtains a lower inform F1 and match rate than PPO, it gets a slight improvement [CONTINUE] on task success [CONTINUE] Ablation test is investigated in Table 3. [CONTINUE] It is perceptible that GDPL has better performance than GDPL-sess on the task success and is comparable regarding the dialog turns, [CONTINUE] GDPL also outperforms GDPL-discr