Leveraging Implicit Feedback from Deployment Data in Dialogue

We study improving social conversational agents by learning from natural dialogue between users and a deployed model, without extra annotations. To implicitly measure the quality of a machine-generated utterance, we leverage signals like user response length, sentiment and reaction of the future human utterances in the collected dialogue episodes. Our experiments use the publicly released deployment data from BlenderBot (Xu et al., 2023). Human evaluation indicates improvements in our new models over baseline responses; however, we find that some proxy …

agents annotations conversational conversational agents cs.cl data deployment dialogue episodes extra feedback future future human generated human machine natural quality sentiment social study