Learning to Detect Relevant Contexts and Knowledge for Response Selection in Retrieval-based Dialogue Systems
Year: 2020
Type: conference-paper
Abstract: 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.
Authors: Kai Hua, Zhiyuan Feng, Chongyang Tao, Rui Yan, Lu Zhang
Institutions: Peking University, Beijing Academy of Artificial Intelligence
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3093680609