Unsupervised Paraphrasing of Multiword Expressions

Takashi Wada, Yuji Matsumoto, Timothy Baldwin, Jey Han Lau


Abstract
We propose an unsupervised approach to paraphrasing multiword expressions (MWEs) in context. Our model employs only monolingual corpus data and pre-trained language models (without fine-tuning), and does not make use of any external resources such as dictionaries. We evaluate our method on the SemEval 2022 idiomatic semantic text similarity task, and show that it outperforms all unsupervised systems and rivals supervised systems.
Anthology ID:
2023.findings-acl.290
Volume:
Findings of the Association for Computational Linguistics: ACL 2023
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4732–4746
Language:
URL:
https://aclanthology.org/2023.findings-acl.290
DOI:
10.18653/v1/2023.findings-acl.290
Bibkey:
Cite (ACL):
Takashi Wada, Yuji Matsumoto, Timothy Baldwin, and Jey Han Lau. 2023. Unsupervised Paraphrasing of Multiword Expressions. In Findings of the Association for Computational Linguistics: ACL 2023, pages 4732–4746, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
Unsupervised Paraphrasing of Multiword Expressions (Wada et al., Findings 2023)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-1/2023.findings-acl.290.pdf