1 research outputs found
A Complex KBQA System using Multiple Reasoning Paths
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