Semantic parsers convert natural language to logical forms, which can be evaluated on knowledge bases (KBs) to produce denotations. Recent semantic parsers have been developed with sequence-to-sequence (seq2seq) pre-trained language models (PLMs) or large language models, where the models treat logical forms as sequences of tokens. For syntactic and semantic validity, the semantic parsers use grammars that enable constrained decoding. However, the grammars lack the ability to utilize large information of KBs, although logical forms contain representations of KB elements, such as entities or relations. In this work, we propose a grammar augmented with candidate expressions for semantic parsing on a large KB with a seq2seq PLM. The grammar defines actions as production rules, and our semantic parser predicts actions during inference under the constraints by types and candidate expressions. We apply the grammar to knowledge base question answering, where the constraints by candidate expressions assist a semantic parser to generate valid KB elements. In experiments on two benchmarks, KQA Pro and Overnight, the constraints by candidate expressions increased the accuracy of our semantic parser, whether it was trained with strong supervision or weak supervision. Our semantic parser achieved state-of-the-art accuracies on KQA Pro and Overnight.

本研究解决了现有语义解析器在知识库问答中未能有效利用知识库大信息的问题。论文提出了一种增强语法的语义解析方法，利用候选表达式来生成有效的知识库元素。实验结果表明，该方法在两个基准测试上达到了最先进的准确率，显著提高了解析器的性能。

知识库问答的候选表达式语义解析