Deep Reinforcement Learning for Dialogue Generation
Table 2: The average number of simulated turns from standard Seq2Seq models, mutual information model and the proposed RL model.
['Model', '# of simulated turns']
The test set consists of 1,000 input messages. To reduce the risk of circular dialogues, we limit the number of simulated turns to be less than 8. As can be seen, using mutual information leads to more sustained conversations between the two agents. The proposed RL model is first trained based on the mutual information objective and thus benefits from it in addition to the RL model. We observe that the RL model with dialogue simulation achieves the best evaluation score.