GraphQ IR: Unifying the Semantic Parsing of Graph Query Languages with One Intermediate Representation
Lunyiu Nie, Shulin Cao, Jiaxin Shi, Jiuding Sun, Qi Tian, Lei Hou, Juanzi Li, Jidong Zhai
Abstract
Subject to the huge semantic gap between natural and formal languages, neural semantic parsing is typically bottlenecked by its complexity of dealing with both input semantics and output syntax. Recent works have proposed several forms of supplementary supervision but none is generalized across multiple formal languages. This paper proposes a unified intermediate representation for graph query languages, named GraphQ IR. It has a natural-language-like expression that bridges the semantic gap and formally defined syntax that maintains the graph structure. Therefore, a neural semantic parser can more precisely convert user queries into GraphQ IR, which can be later losslessly compiled into various downstream graph query languages. Extensive experiments on several benchmarks including KQA Pro, Overnight, GrailQA, and MetaQA-Cypher under the standard i.i.d., out-of-distribution, and low-resource settings validate GraphQ IR’s superiority over the previous state-of-the-arts with a maximum 11% accuracy improvement.
BibTeX
@inproceedings{nie-etal-2022-graphq,
title = "{G}raph{Q} {IR}: Unifying the Semantic Parsing of Graph Query Languages with One Intermediate Representation",
author = "Nie, Lunyiu and
Cao, Shulin and
Shi, Jiaxin and
Sun, Jiuding and
Tian, Qi and
Hou, Lei and
Li, Juanzi and
Zhai, Jidong",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.394/",
doi = "10.18653/v1/2022.emnlp-main.394",
pages = "5848--5865"
}