  Neural conversation models are attractive because one can train a model
directly on dialog examples with minimal labeling. With a small amount of data,
however, they often fail to generalize over test data since they tend to
capture spurious features instead of semantically meaningful domain knowledge.
To address this issue, we propose a novel approach that allows any human
teachers to transfer their domain knowledge to the conversation model in the
form of natural language rules. We tested our method with three different
dialog datasets. The improved performance across all domains demonstrates the
efficacy of our proposed method.
