Year: 2023
Type: conference-paper
Abstract: In this paper, we propose an unsupervised query enhanced approach for knowledgeintensive conversations, namely QKConv.There are three modules in QKConv: a query generator, an off-the-shelf knowledge selector, and a response generator.QKConv is optimized through joint training, which produces the response by exploring multiple candidate queries and leveraging corresponding selected knowledge.The joint training solely relies on the dialogue context and target response, getting exempt from extra query annotations or knowledge provenances.To evaluate the effectiveness of the proposed QKConv, we conduct experiments on three representative knowledgeintensive conversation datasets: conversational question-answering, task-oriented dialogue, and knowledge-grounded conversation.Experimental results reveal that QKConv performs better than all unsupervised methods across three datasets and achieves competitive performance compared to supervised methods.
Institution: Baidu (China)
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4385571997