  Neural dialogue models suffer from low-quality responses when interacted in
practice, demonstrating difficulty in generalization beyond training data.
Recently, knowledge distillation has been used to successfully regularize the
student by transferring knowledge from the teacher. However, the teacher and
the student are trained on the same dataset and tend to learn similar feature
representations, whereas the most general knowledge should be found through
differences. The finding of general knowledge is further hindered by the
unidirectional distillation, as the student should obey the teacher and may
discard some knowledge that is truly general but refuted by the teacher. To
this end, we propose a novel training framework, where the learning of general
knowledge is more in line with the idea of reaching consensus, i.e., finding
common knowledge that is beneficial to different yet all datasets through
diversified learning partners. Concretely, the training task is divided into a
group of subtasks with the same number of students. Each student assigned to
one subtask not only is optimized on the allocated subtask but also imitates
multi-view feature representation aggregated from other students (i.e., student
peers), which induces students to capture common knowledge among different
subtasks and alleviates the over-fitting of students on the allocated subtasks.
To further enhance generalization, we extend the unidirectional distillation to
the bidirectional distillation that encourages the student and its student
peers to co-evolve by exchanging complementary knowledge with each other.
Empirical results and analysis demonstrate that our training framework
effectively improves the model generalization without sacrificing training
efficiency.
