Knowledge base question answering (KBQA)is an important task in Natural
Language Processing. Existing approaches face significant challenges including
complex question understanding, necessity for reasoning, and lack of large
end-to-end training datasets. In this work, we propose Neuro-Symbolic Question
Answering (NSQA), a modular KBQA system, that leverages (1) Abstract Meaning
Representation (AMR) parses for task-independent question understanding; (2) a
simple yet effective graph transformation approach to convert AMR parses into
candidate logical queries that are aligned to the KB; (3) a pipeline-based
approach which integrates multiple, reusable modules that are trained
specifically for their individual tasks (semantic parser, entity
andrelationship linkers, and neuro-symbolic reasoner) and do not require
end-to-end training data. NSQA achieves state-of-the-art performance on two
prominent KBQA datasets based on DBpedia (QALD-9 and LC-QuAD1.0). Furthermore,
our analysis emphasizes that AMR is a powerful tool for KBQA systems.

本文提出了一种模块化的知识库问答系统 Neuro-Symbolic Question Answering (NSQA)，它采用 Abstract Meaning Representation (AMR) parse 语义分析、简单有效的图转换方法和多个可重复使用训练模块（语义分析、实体和关系链接和神经符号推理器），避免需要端到端的训练数据，并在两个著名知识库问答数据集上（基于 DBpedia 的 QALD-9 和 LC-QuAD1.0）实现了最先进的性能，并且 AMR 是 KBQA 系统的强大工具。