Diversifying Neural Dialogue Generation via Negative Distillation
Yiwei Li, Shaoxiong Feng, Bin Sun, Kan Li · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · 2022
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 querywise 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. 1