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Robots will eventually be part of every household. It is thus critical toenable algorithms to learn from and be guided by non-expert users. In thispaper, we bring a human in the loop, and enable a human teacher to givefeedback to a learning agent in the form of natural language. We argue that adescriptive sentence can provide a much stronger learning signal than a numericreward in that it can easily point to where the mistakes are and how to correctthem. We focus on the problem of image captioning in which the quality of theoutput can easily be judged by non-experts. We propose a hierarchicalphrase-based captioning model trained with policy gradients, and design afeedback network that provides reward to the learner by conditioning on thehuman-provided feedback. We show that by exploiting descriptive feedback ourmodel learns to perform better than when given independently written humancaptions.

Teaching Machines to Describe Images via Natural Language Feedback