Improving scripts with a memory of natural feedback
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How can an end-user provide feedback if a deployed structured prediction model generates incorrect output? Our goal is to allow users to correct errors directly through interaction, without retraining, by giving feedback on the model's output. We create a dynamic memory architecture with a growing memory of feedbacks about errors in the output. Given a new, unseen input, our model can use feedback from a similar, past erroneous state. On a script generation task, we show empirically that the model learns to apply feedback effectively (up to 30 points improvement), while avoiding similar past mistakes after deployment (up to 10 points improvement on an unseen set). This is a first step towards strengthening deployed models, potentially broadening their utility.
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