ACL 2023findings1 citations
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.
BibTeX
@inproceedings{wada-etal-2023-unsupervised,
title = "Unsupervised Paraphrasing of Multiword Expressions",
author = "Wada, Takashi and
Matsumoto, Yuji and
Baldwin, Timothy and
Lau, Jey Han",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.290/",
doi = "10.18653/v1/2023.findings-acl.290",
pages = "4732--4746"
}