论文标题
使用多个推理路径的复杂KBQA系统
A Complex KBQA System using Multiple Reasoning Paths
论文作者
论文摘要
基于多跳的知识的问题回答（KBQA）是自然语言理解的复杂任务。近年来，已经提出了许多KBQA方法，并且大多数方法是根据标记的推理路径进行培训的。这阻碍了系统的性能，因为许多正确的推理路径没有将其标记为地面真理，因此无法学习。在本文中，我们引入了一个端到端的KBQA系统，该系统可以利用多个推理路径的信息，并且只需要标记为答案作为监督。我们在包含单跳简单问题的几个基准数据集上进行实验，以及Muti-Hop复杂问题，包括WebQueStionsP（WQSP），Complex Webquestion-1.1（CWQ）和PathQueStion-Large（PQL）（PQL），并表现出强烈的表现。
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.
