Diversifying Neural Dialogue Generation via Negative Distillation
Yiwei Li, Shaoxiong Feng, Bin Sun, Kan Li
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 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.
BibTeX
@inproceedings{li-etal-2022-diversifying,
title = "Diversifying Neural Dialogue Generation via Negative Distillation",
author = "Li, Yiwei and
Feng, Shaoxiong and
Sun, Bin and
Li, Kan",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.31/",
doi = "10.18653/v1/2022.naacl-main.31",
pages = "407--418"
}