Learning Goal-oriented Dialogue Policy with Opposite Agent Awareness
Table 1: The results on MultiWoZ dataset, a large scale multi-domain task-oriented dialog dataset. We used a rule-based method for DST and Agenda-based user simulator. The DQN method can be regard as OPPA w/o OBE. Human-human performance from the test set serves as the upper bound.
['Method', '#Turn', 'Inform F1', 'Match', 'Success']
The results on MultiWOZ dataset are shown in Table By first infer the next action of the opposite agent, the target agent policy can make better choices to match the reward signal during training. When compared with human performance, OPPA even achieves a higher success rate, although the number of turns is still higher. This might be due to the fact that the user is sensitive to the dialogue length. When a dialogue becomes intolerably long, many user will leave without completing the dialogue. By taking actions in account of the inferred opposite action, the target agent can also make the dialogue more efficiently by avoiding some lengthy interactions, which is extremely important in applications where the user is sensitive to dialogue length.