@inproceedings{wang-etal-2022-new,
    title = "A New Concept of Knowledge based Question Answering ({KBQA}) System for Multi-hop Reasoning",
    author = "Wang, Yu  and
      Srinivasan, Vijay  and
      Jin, Hongxia",
    editor = "Carpuat, Marine  and
      de Marneffe, Marie-Catherine  and
      Meza Ruiz, Ivan Vladimir",
    booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2022.naacl-main.294/",
    doi = "10.18653/v1/2022.naacl-main.294",
    pages = "4007--4017",
    abstract = "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 \textbf{M}utliple \textbf{R}easoning \textbf{P}aths KB\textbf{QA} System (MRP-QA). 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."
}