The learning algorithm starts from training with pseudo ground-truth that is constructed by making full use of responses as an alternation of human annotations, and then alternatively updates the knowledge selection model and the response generation model through a reinforcement learning approach and a curriculum learning approach respectively.
knowledge selection is further optimized with the feedback from response generation, and the knowledge used for fine-tuning the response generation model gradually moves from the pseudo ground-truth to the prediction of the knowledge selection module.