Bootstrapping a Neural Conversational Agent with Dialogue Self-Play, Crowdsourcing and On-Line Reinforcement Learning (N18-3)
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| Challenge: | End-to-end neural models for conversational agents require large corpus of dialogues to learn effectively. |
| Approach: | They propose a method for building an agent for arbitrary tasks by combining dialogue self-play and crowd-sourcing. |
| Outcome: | The proposed approach can be quickly bootstrapped to deploy in front of users and further optimized via interactive learning from actual users. |
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