Learning Goal-oriented Dialogue Policy with Opposite Agent Awareness
Table 3: The proportion of agreed and Pareto optimal (PO) sessions for our proposed OPPA and the baselines on the negotiation dataset.
['Method', 'vs. SL RNN', 'vs. SL RNN', 'vs. RL RNN', 'vs. RL RNN', 'vs. ROL', 'vs. ROL', 'vs. RL ROL', 'vs. RL ROL']
As we can see, the percentage of Pareto optimal increases in our method, showing that the OPPA model can explore the solution space more effectively. However, the agreement rate decreases when the opposite model gets stronger. This phenomenon is also found in Lewis et al. when they change the opposite agent from SL RNN to real human. This can be attributed to the aggressiveness of the agent: when both agents act aggressively, they are less likely to reach an agreement. The SL RNN model simply imitates the behavior in the dialogue corpus, while the ROL and RL mechanisms both help the agent to explore more spaces, which makes them more aggressive on action selection.