A New Concept of Knowledge based Question Answering (KBQA) System using Multiple Reasoning Paths
Knowledge based question answering (KBQA) is a complex task for natural language understanding. Many KBQA approaches have been proposed in recent years, and most of them are trained based on labeled reasoning path. This hinders the system’s performance as many correct reasoning paths are not labeled as ground truth, and thus they cannot be learned. In this paper, we introduce a new concept of KBQA system which can leverage multiple reasoning paths’ information and only requires labeled answer as supervision. We name it as Mutliple Reasoning Paths KBQA System (MRPQA). We conduct experiments on several benchmark datasets containing both singlehop simple questions as well as muti-hop complex questions, including WebQuestionSP (WQSP), ComplexWebQuestion-1.1 (CWQ), and PathQuestion-Large (PQL), and demonstrate strong performance.
Author: Yu Wang
Published: Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL)
Date: Jul 21, 2022