  The ubiquitous nature of chatbots and their interaction with users generate
an enormous amount of data. Can we improve chatbots using this data? A
self-feeding chatbot improves itself by asking natural language feedback when a
user is dissatisfied with its response and uses this feedback as an additional
training sample. However, user feedback in most cases contains extraneous
sequences hindering their usefulness as a training sample. In this work, we
propose a generative adversarial model that converts noisy feedback into a
plausible natural response in a conversation. The generator's goal is to
convert the feedback into a response that answers the user's previous utterance
and to fool the discriminator which distinguishes feedback from natural
responses. We show that augmenting original training data with these modified
feedback responses improves the original chatbot performance from 69.94% to
75.96% in ranking correct responses on the Personachat dataset, a large
improvement given that the original model is already trained on 131k samples.
