  Although pre-training models have achieved great success in dialogue
generation, their performance drops dramatically when the input contains an
entity that does not appear in pre-training and fine-tuning datasets (unseen
entity). To address this issue, existing methods leverage an external knowledge
base to generate appropriate responses. In real-world scenario, the entity may
not be included by the knowledge base or suffer from the precision of knowledge
retrieval. To deal with this problem, instead of introducing knowledge base as
the input, we force the model to learn a better semantic representation by
predicting the information in the knowledge base, only based on the input
context. Specifically, with the help of a knowledge base, we introduce two
auxiliary training objectives: 1) Interpret Masked Word, which conjectures the
meaning of the masked entity given the context; 2) Hypernym Generation, which
predicts the hypernym of the entity based on the context. Experiment results on
two dialogue corpus verify the effectiveness of our methods under both
knowledge available and unavailable settings.
