When Life Gives You Lemons, Make Cherryade: Converting Feedback from Bad Responses into Good Labels

10/28/2022
by   Weiyan Shi, et al.
0

Deployed dialogue agents have the potential to integrate human feedback to continuously improve themselves. However, humans may not always provide explicit signals when the chatbot makes mistakes during interactions. In this work, we propose Juicer, a framework to make use of both binary and free-form textual human feedback. It works by: (i) extending sparse binary feedback by training a satisfaction classifier to label the unlabeled data; and (ii) training a reply corrector to map the bad replies to good ones. We find that augmenting training with model-corrected replies improves the final dialogue model, and we can further improve performance by using both positive and negative replies through the recently proposed Director model.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset