Learning to Detect Relevant Contexts and Knowledge for Response Selection in Retrieval-based Dialogue Systems
Kai Hua, Zhiyuan Feng, Chongyang Tao, Rui Yan, Lu Zhang · 2020
Recently, knowledge-grounded conversations in the open domain gain great attention from researchers. Existing works on retrieval-based dialogue systems have paid tremendous efforts to utilize neural networks to build a matching model, where all of the context and knowledge contents are used to match the response candidate with various representation methods.