@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",
    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.",
}

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.
Wada, Takashi Matsumoto, Yuji Baldwin, Timothy Lau, Jey Han
Unsupervised Paraphrasing of Multiword Expressions