Zero-Resource Knowledge-Grounded Dialogue Generation

While neural conversation models have shown great potentials towards generating
informative and engaging responses via introducing external knowledge, learning
such a model often requires knowledge-grounded dialogues that are difficult to
obtain. To overcome the data challenge and reduce the cost of building a knowledge grounded dialogue system, we explore the problem under a zero-resource setting
by assuming no context-knowledge-response triples are needed for training. To
this end, we propose representing the knowledge that bridges a context and a
response and the way that the knowledge is expressed as latent variables, and
devise a variational approach that can effectively estimate a generation model
from a dialogue corpus and a knowledge corpus that are independent with each
other. Evaluation results on three benchmarks of knowledge-grounded dialogue
generation indicate that our model can achieve comparable performance with state of-the-art methods that rely on knowledge-grounded dialogues for training, and
exhibits a good generalization ability over different topics and different datasets.
Code is available at https://github.com/nlpxucan/ZRKGC.