Improving Zero-shot Relation Classification via Automatically-acquired Entailment Templates

Mahdi Rahimi, Mihai Surdeanu

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

Abstract

While fully supervised relation classification (RC) models perform well on large-scale datasets, their performance drops drastically in low-resource settings. As generating annotated examples are expensive, recent zero-shot methods have been proposed that reformulate RC into other NLP tasks for which supervision exists such as textual entailment. However, these methods rely on templates that are manually created which is costly and requires domain expertise. In this paper, we present a novel strategy for template generation for relation classification, which is based on adapting Harris’ distributional similarity principle to templates encoded using contextualized representations. Further, we perform empirical evaluation of different strategies for combining the automatically acquired templates with manual templates. The experimental results on TACRED show that our approach not only performs better than the zero-shot RC methods that only use manual templates, but also that it achieves state-of-the-art performance for zero-shot TACRED at 64.3 F1 score.

Original languageEnglish (US)
Title of host publicationACL 2023 - 8th Workshop on Representation Learning for NLP, RepL4NLP 2023 - Proceedings of the Workshop
EditorsBurcu Can, Maximilian Mozes, Samuel Cahyawijaya, Naomi Saphra, Nora Kassner, Shauli Ravfogel, Abhilasha Ravichander, Chen Zhao, Isabelle Augenstein, Anna Rogers, Kyunghyun Cho, Edward Grefenstette, Lena Voita
PublisherAssociation for Computational Linguistics (ACL)
Pages187-195
Number of pages9
ISBN (Electronic)9781959429777
StatePublished - 2023
Externally publishedYes
Event8th Workshop on Representation Learning for NLP, RepL4NLP 2023, co-located with ACL 2023 - Toronto, Canada
Duration: Jul 13 2023 → …

Publication series

NameProceedings of the Annual Meeting of the Association for Computational Linguistics
ISSN (Print)0736-587X

Conference

Conference8th Workshop on Representation Learning for NLP, RepL4NLP 2023, co-located with ACL 2023
Country/TerritoryCanada
CityToronto
Period7/13/23 → …

ASJC Scopus subject areas

  • Computer Science Applications
  • Linguistics and Language
  • Language and Linguistics

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