Abstract
End-to-end neural models for intelligent dialogue
systems suffer from the problem of generating uninformative responses. Various methods were proposed to generate more informative responses by
leveraging external knowledge. However, few previous work has focused on selecting appropriate
knowledge in the learning process. The inappropriate selection of knowledge could prohibit the model
from learning to make full use of the knowledge.
Motivated by this, we propose an end-to-end neural model which employs a novel knowledge selection mechanism where both prior and posterior
distributions over knowledge are used to facilitate
knowledge selection. Specifically, a posterior distribution over knowledge is inferred from both utterances and responses, and it ensures the appropriate selection of knowledge during the training process. Meanwhile, a prior distribution, which is inferred from utterances only, is used to approximate
the posterior distribution so that appropriate knowledge can be selected even without responses during
the inference process. Compared with the previous
work, our model can better incorporate appropriate
knowledge in response generation. Experiments on
both automatic and human evaluation verify the superiority of our model over previous baselines