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Unsupervised Paraphrasing of Multiword Expressions

About

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.

Takashi Wada, Yuji Matsumoto, Timothy Baldwin, Jey Han Lau• 2023

Related benchmarks

TaskDatasetResultRank
Semantic Textual SimilarityEnglish STS
Average Score76.31
68
Semantic Textual SimilaritySemEval-2022 Task 2 Idiomatic STS (evaluation)
Spearman Rho (All)0.6613
14
Semantic Textual SimilaritySTS English (test)
Spearman's ρ76.9
9
MWE ParaphrasingEnglish MWE SemEval (test)
P@110.8
9
Semantic Textual SimilaritySemEval STS Portuguese (PT)
Overall Score73.97
3
Semantic Textual SimilaritySemEval STS Galician (GL)
MWE Score34.74
3
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