Program Transfer and Ontology Awareness for Semantic Parsing in KBQA
  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.
