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
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2023.findings-acl.290.pdf