FAME: Flexible, Scalable Analogy Mappings Engine

Shahar Jacob, Chen Shani, Dafna Shahaf

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Analogy is one of the core capacities of human cognition; when faced with new situations, we often transfer prior experience from other domains. Most work on computational analogy relies heavily on complex, manually crafted input. In this work, we relax the input requirements, requiring only names of entities to be mapped. We automatically extract commonsense representations and use them to identify a mapping between the entities. Unlike previous works, our framework can handle partial analogies and suggest new entities to be added. Moreover, our method's output is easily interpretable, allowing for users to understand why a specific mapping was chosen. Experiments show that our model correctly maps 81.2% of classical 2x2 analogy problems (guess level=50%). On larger problems, it achieves 77.8% accuracy (mean guess level=13.1%). In another experiment, we show our algorithm outperforms human performance, and the automatic suggestions of new entities resemble those suggested by humans. We hope this work will advance computational analogy by paving the way to more flexible, realistic input requirements, with broader applicability.

Original languageAmerican English
Title of host publicationEMNLP 2023 - 2023 Conference on Empirical Methods in Natural Language Processing, Proceedings
EditorsHouda Bouamor, Juan Pino, Kalika Bali
PublisherAssociation for Computational Linguistics (ACL)
Pages16426-16442
Number of pages17
ISBN (Electronic)9798891760608
StatePublished - 2023
Event2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023 - Hybrid, Singapore, Singapore
Duration: 6 Dec 202310 Dec 2023

Publication series

NameEMNLP 2023 - 2023 Conference on Empirical Methods in Natural Language Processing, Proceedings

Conference

Conference2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023
Country/TerritorySingapore
CityHybrid, Singapore
Period6/12/2310/12/23

Bibliographical note

Publisher Copyright:
©2023 Association for Computational Linguistics.

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