Modelling commonsense commonalities with multi-facet concept embeddings

Kteich, Hanane, Li, Na, Chatterjee, Usashi, Bouraoui, Zied and Schockaert, Steven ORCID: https://orcid.org/0000-0002-9256-2881 2024. Modelling commonsense commonalities with multi-facet concept embeddings. Presented at: The 62nd Annual Meeting of the Association for Computational Linguistics, Bangkok, Thailand, 11-16 August 2024. Published in: Ku, Lun-Wei, Martins, Andre and Srikumar, Vivek eds. Findings of the Association for Computational Linguistics ACL 2024. Association for Computational Linguistics, pp. 1467-1480.

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Abstract

Concept embeddings offer a practical and efficient mechanism for injecting commonsense knowledge into downstream tasks. Their core purpose is often not to predict the commonsense properties of concepts themselves, but rather to identify commonalities, i.e. sets of concepts which share some property of interest. Such commonalities are the basis for inductive generalisation, hence high-quality concept embeddings can make learning easier and more robust. Unfortunately, standard embeddings primarily reflect basic taxonomic categories, making them unsuitable for finding commonalities that refer to more specific aspects (e.g. the colour of objects or the materials they are made of). In this paper, we address this limitation by explicitly modelling the different facets of interest when learning concept embeddings. We show that this leads to embeddings which capture a more diverse range of commonsense properties, and consistently improves results in downstream tasks such as ultra-fine entity typing and ontology completion.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	Association for Computational Linguistics
Date of First Compliant Deposit:	30 July 2024
Date of Acceptance:	16 May 2024
Last Modified:	19 Aug 2024 09:46
URI:	https://orca.cardiff.ac.uk/id/eprint/170093

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