Leveraging Abstract Meaning Representation for Knowledge Base Question Answering
This work addresses the challenge of complex question understanding and the need for reasoning in Knowledge Base Question Answering, which is an incremental improvement for NLP researchers working on KBQA systems.
This paper introduces Neuro-Symbolic Question Answering (NSQA), a modular system that uses Abstract Meaning Representation (AMR) to understand questions and convert them into logical queries for knowledge bases. NSQA achieves state-of-the-art performance on the QALD-9 and LC-QuAD1.0 datasets.
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.