DukeNet: A Dual Knowledge Interaction Network for Knowledge-Grounded Conversation

Today's conversational agents often generate responses that not sufficiently informative. One way of making them more informative is through the use of of external knowledge sources with so-called Knowledge-Grounded Conversations (KGCs). In this paper, we target the Knowledge Selection (KS) task, a key ingredient in KGC, that is aimed at selecting the appropriate knowledge to be used in the next response. Existing approaches to Knowledge Selection (KS) based on learned representations of the conversation context, that is previous conversation turns, and use Maximum Likelihood Estimation (MLE) to optimize KS. Such approaches have two main limitations. First, they do not explicitly track what knowledge has been used in the conversation nor how topics have shifted during the conversation. Second, MLE often relies on a limited set of example conversations for training, from which it is hard to infer that facts retrieved from the knowledge source can be re-used in multiple conversation contexts, and vice versa. We propose Dual Knowledge Interaction Network (DukeNet), a framework to address these challenges. DukeNet explicitly models knowledge tracking and knowledge shifting as dual tasks. We also design Dual Knowledge Interaction Learning (DukeL), an unsupervised learning scheme to train DukeNet by facilitating interactions between knowledge tracking and knowledge shifting, which, in turn, enables DukeNet to explore extra knowledge besides the knowledge encountered in the training set. This dual process also allows us to define rewards that help us to optimize both knowledge tracking and knowledge shifting. Experimental results on two public KGC benchmarks show that DukeNet significantly outperforms state-of-the-art methods in terms of both automatic and human evaluations, indicating that DukeNet enhanced by DukeL can select more appropriate knowledge and hence generate more informative and engaging responses.