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The ubiquitous nature of chatbots and their interaction with users generatean enormous amount of data. Can we improve chatbots using this data? Aself-feeding chatbot improves itself by asking natural language feedback when auser is dissatisfied with its response and uses this feedback as an additionaltraining sample. However, user feedback in most cases contains extraneoussequences hindering their usefulness as a training sample. In this work, wepropose a generative adversarial model that converts noisy feedback into aplausible natural response in a conversation. The generator's goal is toconvert the feedback into a response that answers the user's previous utteranceand to fool the discriminator which distinguishes feedback from naturalresponses. We show that augmenting original training data with these modifiedfeedback responses improves the original chatbot performance from 69.94% to75.96% in ranking correct responses on the Personachat dataset, a largeimprovement given that the original model is already trained on 131k samples.

Learning Improvised Chatbots from Adversarial Modifications of Natural Language Feedback