Papers by Gabriel Gordon-Hall
Learning Dialog Policies from Weak Demonstrations (2020.acl-main)
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| Challenge: | Existing methods to train dialog managers struggle with large state spaces and sparse rewards. |
| Approach: | They propose a deep reinforcement learning algorithm that uses dialog data to guide the agent to successfully respond to a user's requests. |
| Outcome: | Experiments in a multi-domain dialog system framework validate our methods and get high success rates even when trained on out-of-domain data. |