Towards Collaborative Neural-Symbolic Graph Semantic Parsing via Uncertainty

Zi Lin, Jeremiah Zhe Liu, Jingbo Shang

Abstract
Recent work in task-independent graph semantic parsing has shifted from grammar-based symbolic approaches to neural models, showing strong performance on different types of meaning representations. However, it is still unclear that what are the limitations of these neural parsers, and whether these limitations can be compensated by incorporating symbolic knowledge into model inference. In this paper, we address these questions by taking English Resource Grammar (ERG) parsing as a case study. Specifically, we first develop a state-of-the-art, T5-based neural ERG parser, and conduct detail analyses of parser performance within fine-grained linguistic categories. The neural parser attains superior performance on in-distribution test set, but degrades significantly on long-tail situations, while the symbolic parser performs more robustly. To address this, we further propose a simple yet principled collaborative framework for neural-symbolic semantic parsing, by designing a decision criterion for beam search that incorporates the prior knowledge from a symbolic parser and accounts for model uncertainty. Experimental results show that the proposed framework yields comprehensive improvement over neural baseline across long-tail categories, yielding the best known Smatch score (97.01) on the well-studied DeepBank benchmark.
Anthology ID:
2022.findings-acl.328
Volume:
Findings of the Association for Computational Linguistics: ACL 2022
Month:
May
Year:
2022
Address:
Dublin, Ireland
Editors:
Smaranda Muresan, Preslav Nakov, Aline Villavicencio
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4160–4173
Language:
URL:
https://aclanthology.org/2022.findings-acl.328
DOI:
10.18653/v1/2022.findings-acl.328
Bibkey:
Cite (ACL):
Zi Lin, Jeremiah Zhe Liu, and Jingbo Shang. 2022. Towards Collaborative Neural-Symbolic Graph Semantic Parsing via Uncertainty. In Findings of the Association for Computational Linguistics: ACL 2022, pages 4160–4173, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Towards Collaborative Neural-Symbolic Graph Semantic Parsing via Uncertainty (Lin et al., Findings 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/naacl-24-ws-corrections/2022.findings-acl.328.pdf
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