A Complex KBQA System using Multiple Reasoning Paths
This addresses the limitation in KBQA where unlabeled correct reasoning paths hinder performance, offering a solution for natural language understanding tasks.
The paper tackles the problem of multi-hop knowledge-based question answering by introducing an end-to-end system that leverages multiple reasoning paths using only labeled answers for supervision, achieving strong performance on benchmark datasets like WebQuestionSP, ComplexWebQuestion-1.1, and PathQuestion-Large.
Multi-hop 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 an end-to-end KBQA system which can leverage multiple reasoning paths' information and only requires labeled answer as supervision. We conduct experiments on several benchmark datasets containing both single-hop simple questions as well as muti-hop complex questions, including WebQuestionSP (WQSP), ComplexWebQuestion-1.1 (CWQ), and PathQuestion-Large (PQL), and demonstrate strong performance.