Deep Reinforcement Learning with Hierarchical Action Exploration for Dialogue Generation (2024.lrec-main)
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| Challenge: | Existing approaches to improve dialogues with random sampling are inefficient due to the large number of eligible responses with high action values. |
| Approach: | They propose a dual-granularity Q-function that extracts actions based on a grained hierarchy . they use offline RL and learn from multiple reward functions designed to capture emotional nuances in human interactions. |
| Outcome: | The proposed approach outperforms baselines across automatic metrics and human evaluations. |
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