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Program Transfer and Ontology Awareness for Semantic Parsing in KBQA

2021-10-12 05:25:30
Shulin Cao, Jiaxin Shi, Zijun Yao, Lei Hou, Juanzi Li

Abstract

Semantic parsing in KBQA aims to parse natural language questions into logical forms, whose execution against a knowledge base produces answers. Learning semantic parsers from question-answer pairs requires searching over a huge space of logical forms for ones consistent with answers. Current methods utilize various prior knowlege or entity-level KB constraints to reduce the search space. In this paper, we investigate for the first time prior knowledge from external logical form annotations and ontology-level constraints. We design a hierarchical architecture for program transfer, and propose an ontology-guided pruning algorithm to reduce the search space. The experiments on ComplexWebQuestions show that our method improves the state-of-the-art F1 score from 44.0% to 58.7%, with an absolute gain of 14.7%, which demonstrates the effectiveness of program transfer and ontology awareness.

Abstract (translated)

URL

https://arxiv.org/abs/2110.05743

PDF

https://arxiv.org/pdf/2110.05743.pdf


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