Nudging Neural Conversational Model with Domain Knowledge

ABSTRACT Neural conversation models are attractive because one can train a model directly on dialog examples with minimal labeling. Witha 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. Toaddress this issue, we propose a novel approach that allows any human teachers to transfer their domain knowledge tothe 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. Index Terms-- conversational agents, domain knowledge, naturallanguage rule, neural conversational model 1. INTRODUCTION Recently, conversational systems have been increasingly adopting neural approaches [1, 2, 3, 4, 5, 6, 7].