Unsupervised Paraphrasing of Multiword Expressions

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

Findings: Semantics: Lexical Findings Paper

Session 1: Semantics: Lexical (Virtual Poster)
Conference Room: Pier 7&8
Conference Time: July 10, 11:00-12:30 (EDT) (America/Toronto)
Global Time: July 10, Session 1 (15:00-16:30 UTC)
Spotlight Session: Spotlight - Metropolitan West (Spotlight)
Conference Room: Metropolitan West
Conference Time: July 10, 19:00-21:00 (EDT) (America/Toronto)
Global Time: July 10, Spotlight Session (23:00-01:00 UTC)
Keywords: multi-word expressions, paraphrasing
TLDR: 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 SemEv...
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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.