Before revision:  (4) system of systems approach , which integrates multiple, reusable modules that are trained specifically for their individual tasks ( e.g. semantic parsing, entity linking, and relationship linking ) and do not require end-to-end training data. NSQA achieves state-of-the-art performance on  QALD-9 and LC-QuAD 1.0. NSQA's novelty lies in its modular neuro-symbolic architecture and its task-general approach to interpreting natural language questions .
After revision:  (4) system of systems approach , which integrates multiple, reusable modules that are trained specifically for their individual tasks ( e.g. semantic parsing, entity linking, and relationship linking ) 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-QuAD 1.0. NSQA's novelty lies in its modular neuro-symbolic architecture and its task-general approach to interpreting natural language questions .