A Three-Stage Learning Framework for Low-Resource Knowledge-Grounded Dialogue Generation
Shilei Liu, Xiaofeng Zhao, Bochao Li, Feiliang Ren, Longhui Zhang, Shujuan Yin · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · 2021
Neural conversation models have shown great potentials towards generating fluent and informative responses by introducing external background knowledge.Nevertheless, it is laborious to construct such knowledge-grounded dialogues, and existing models usually perform poorly when transfer to new domains with limited training samples.Therefore, building a knowledge-grounded dialogue system under the low-resource setting is a still crucial issue.In this paper, we propose a novel threestage learning framework based on weakly supervised learning which benefits from large scale ungrounded dialogues and unstructured knowledge base.To better cooperate with this framework, we devise a variant of Transformer with decoupled decoder which facilitates the disentangled learning of response generation and knowledge incorporation.Evaluation results on two benchmarks indicate that our approach can outperform other state-of-the-art methods with less training data, and even in zero-resource scenario, our approach still performs well.