Nudging Neural Conversational Model with Domain Knowledge
This addresses the issue of poor generalization in conversational AI for domains with limited data, though it is incremental as it builds on existing neural models.
The authors tackled the problem of neural conversation models failing to generalize with small datasets by proposing a method to transfer domain knowledge via natural language rules, resulting in improved performance across three dialog datasets.
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.