  Generative dialogue models suffer badly from the generic response problem,
limiting their applications to a few toy scenarios. Recently, an interesting
approach, namely negative training, has been proposed to alleviate this problem
by reminding the model not to generate high-frequency responses during
training. However, its performance is hindered by two issues, ignoring
low-frequency but generic responses and bringing low-frequency but meaningless
responses. In this paper, we propose a novel negative training paradigm, called
negative distillation, to keep the model away from the undesirable generic
responses while avoiding the above problems. First, we introduce a negative
teacher model that can produce query-wise generic responses, and then the
student model is required to maximize the distance with multi-level negative
knowledge. Empirical results show that our method outperforms previous negative
training methods significantly.
