Hierarchical Text Generation and Planning for Strategic Dialogue
Table 2: Comparison of different models based on their end-task reward. Our clusters substantially improve reward, indicating that they make it easier for supervised learning to model strategic decision making. The size of 95% CI is within 0.14 for each entry.
['Model', 'Score vs. RNN', 'Score vs. Hierarchical']
Task Performance We measure the performance of the different models on their end-task reward over 1000 negotiations in self-play. We find that the use of our latent representations leads to a large improvement in the reward, indicating that our representations make it easier for the supervised model to learn the latent decision making process in the human dialogues it was trained on.