It is often challenging for a system to solve a new complex problem from scratch, but much easier if the system can access other similar problems and description of their solutions -- a paradigm known as case-based reasoning (CBR). We propose a neuro-symbolic CBR approach for question answering over large knowledge bases (CBR-KBQA). While the idea of CBR is tempting, composing a solution from cases is nontrivial, when individual cases only contain partial logic to the full solution. To resolve this, CBR-KBQA consists of two modules: a non-parametric memory that stores cases (question and logical forms) and a parametric model which can generate logical forms by retrieving relevant cases from memory. Through experiments, we show that CBR-KBQA can effectively derive novel combination of relations not presented in case memory that is required to answer compositional questions. On several KBQA datasets that test compositional generalization, CBR-KBQA achieves competitive performance. For example, on the challenging ComplexWebQuestions dataset, CBR-KBQA outperforms the current state of the art by 11% accuracy. Furthermore, we show that CBR-KBQA is capable of using new cases \emph{without} any further training. Just by incorporating few human-labeled examples in the non-parametric case memory, CBR-KBQA is able to successfully generate queries containing unseen KB relations.
翻译:系统从零开始解决新的复杂问题往往具有挑战性,但如果系统能够获取其他类似问题并描述其解决方案 -- -- 一个被称为基于案例的推理(CBR)的范例。我们建议对大型知识库(CDR-KBQA)的问答采用神经共振共振共振法。虽然CBR的想法是诱人,但从个案中形成一种解决办法却并非偶然的,因为个别个案只包含全部解决方案的部分逻辑。为了解决这个问题,CBR-KBQA由两个模块组成:一个非参数的存储案例的非参数记忆(问题和逻辑形式)和一个通过从记忆中检索相关案例而产生逻辑形式的参数模型。我们通过实验,我们显示CBR-KBQA可以有效地产生新式的关系组合,而对于解答构成问题的记忆中则没有出现新式的组合。关于测试组合的单个案例,CBBR-KQA能够成功地生成复杂网络数据集, CBB-BA的轨迹标定度模型,通过我们无法再用 CBA-C-C-C-BA的精确性案例来显示当前艺术-C-C-BBBR的准确性案例。 展示任何C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-BR-C-C-C-C-C-B-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C