Best of Both Worlds: A Pliable and Generalizable Neuro-Symbolic Approach for Relation Classification

Robert Vacareanu, Fahmida Alam, Md Asiful Islam, Haris Riaz, Mihai Surdeanu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

This paper introduces a novel neuro-symbolic architecture for relation classification (RC) that combines rule-based methods with contemporary deep learning techniques. This approach capitalizes on the strengths of both paradigms: the adaptability of rule-based systems and the generalization power of neural networks. Our architecture consists of two components: a declarative rule-based model for transparent classification and a neural component to enhance rule generalizability through semantic text matching. Notably, our semantic matcher is trained in an unsupervised domain-agnostic way, solely with synthetic data. Further, these components are loosely coupled, allowing for rule modifications without retraining the semantic matcher. In our evaluation, we focused on two few-shot relation classification datasets: Few-Shot TACRED and a Few-Shot version of NYT29. We show that our proposed method outperforms previous state-of-the-art models in three out of four settings, despite not seeing any human-annotated training data. Further, we show that our approach remains modular and pliable, i.e., the corresponding rules can be locally modified to improve the overall model. Human interventions to the rules for the TACRED relation org:parents boost the performance on that relation by as much as 26% relative improvement, without negatively impacting the other relations, and without retraining the semantic matching component.

Original languageEnglish (US)
Title of host publicationFindings of the Association for Computational Linguistics
Subtitle of host publicationNAACL 2024 - Findings
EditorsKevin Duh, Helena Gomez, Steven Bethard
PublisherAssociation for Computational Linguistics (ACL)
Pages2576-2594
Number of pages19
ISBN (Electronic)9798891761193
DOIs
StatePublished - 2024
Event2024 Findings of the Association for Computational Linguistics: NAACL 2024 - Mexico City, Mexico
Duration: Jun 16 2024Jun 21 2024

Publication series

NameFindings of the Association for Computational Linguistics: NAACL 2024 - Findings

Conference

Conference2024 Findings of the Association for Computational Linguistics: NAACL 2024
Country/TerritoryMexico
CityMexico City
Period6/16/246/21/24

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Software

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