Tayyar Madabushi, H., Gow-Smith, E., Scarton, C. et al. (1 more author) (2021) AStitchInLanguageModels : dataset and methods for the exploration of idiomaticity in pre-trained language models. In: Moens, M.-F., Huang, X., Specia, L. and Yih, S.W.-T., (eds.) Findings of the Association for Computational Linguistics: EMNLP 2021. Findings of the Association for Computational Linguistics: EMNLP 2021, 07-11 Nov 2021, Punta Cana, Dominican Republic. Association for Computational Linguistics , pp. 3464-3477. ISBN 9781955917100
Abstract
Despite their success in a variety of NLP tasks, pre-trained language models, due to their heavy reliance on compositionality, fail in effectively capturing the meanings of multiword expressions (MWEs), especially idioms. Therefore, datasets and methods to improve the representation of MWEs are urgently needed. Existing datasets are limited to providing the degree of idiomaticity of expressions along with the literal and, where applicable, (a single) non-literal interpretation of MWEs. This work presents a novel dataset of naturally occurring sentences containing MWEs manually classified into a fine-grained set of meanings, spanning both English and Portuguese. We use this dataset in two tasks designed to test i) a language model’s ability to detect idiom usage, and ii) the effectiveness of a language model in generating representations of sentences containing idioms. Our experiments demonstrate that, on the task of detecting idiomatic usage, these models perform reasonably well in the one-shot and few-shot scenarios, but that there is significant scope for improvement in the zero-shot scenario. On the task of representing idiomaticity, we find that pre-training is not always effective, while fine-tuning could provide a sample efficient method of learning representations of sentences containing MWEs.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2021 Association for Computational Linguistics. Licensed on a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/). |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Funding Information: | Funder Grant number Engineering and Physical Sciences Research Council EP/T02450X/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 10 Mar 2022 09:12 |
Last Modified: | 11 Mar 2022 12:47 |
Status: | Published |
Publisher: | Association for Computational Linguistics |
Refereed: | Yes |
Identification Number: | 10.18653/v1/2021.findings-emnlp.294 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:184561 |