Learning from Dialogue after Deployment: Feed Yourself, Chatbot!

Understand

The majority of conversations a dialogue agent sees over its lifetime occur after it has already been trained and deployed, leaving a vast store of potential training signal untapped.

• In this work, we propose the self-feeding chatbot, a dialogue agent with the ability to extract new training examples from the conversations it participates in.
• As our agent engages in conversation, it also estimates user satisfaction in its responses.
• When the conversation appears to be going well, the user's responses become new training examples to imitate.