Year: 2022
Type: conference-paper
Abstract: 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
Authors: Yiwei Li, Shaoxiong Feng, Bin Sun, Kan Li
Institution: Beijing Institute of Technology
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Awards: G3040904436, 个性化情感图像分类与生成技术研究, G7730730377
OpenAlex ID: W4229014147