One-Shot Exemplification Modeling via Latent Sense Representations

John Harvill, Hee Suk Yoon, Eunseop Yoon, Mark Hasegawa-Johnson, Chang D. Yoo

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


Exemplification modeling is a recently proposed task that aims to produce a viable sentence using a target word that takes on a specific meaning. This task can be particularly challenging for polysemous words since they can have multiple meanings. In this paper, we propose a one-shot variant of the exemplification modeling task such that labeled data is not needed during training, making it possible to train our system using a raw text corpus. Given one example at test time, our proposed approach can generate diverse and fluent examples where the target word accurately matches its intended meaning. We compare our approach to a fully-supervised baseline trained with different amounts of data and focus our evaluation on polysemous words. We use both automatic and human evaluations to demonstrate how each model performs on both seen and unseen words. Our proposed approach performs similarly to the fully-supervised baseline despite not using labeled data during training.

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)
Number of pages12
ISBN (Electronic)9781959429777
StatePublished - 2023
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


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

ASJC Scopus subject areas

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


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