Variational Reward Estimator Bottleneck: Learning Robust Reward Estimator for Multi-Domain Task-Oriented Dialog
Table 1: Results of Agenda-based and VHUS-based user simulators.
['Model', 'Agenda Turns', 'Agenda Match', 'Agenda Inform', 'Agenda Success', 'VHUS Turns', 'VHUS Match', 'VHUS Inform', 'VHUS Success']
In the agenda-based setting, we observe that our proposed method achieves a new state-of-the-art performance. Note that an outstanding model should obtain high scores in every metric, not just a single one, because to regard a dialog as having ended successfully, every request should be informed precisely, thereby guiding a dialog through the user goal. Although GDPL achieves the highest score in Inform F1, our proposed model acts more human-like with respect to Turns, and provides more accurate slot values and matched-entities than the other methods. In VHUS setting, on the other hand, though PPO behaves more human-like in Turns, PPO exhibits greater difficulty in providing accurate information, while our model doesn’t because our method constrains unproductive information flows.