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
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Semantic Textual Similarity | English STS | Average Score76.31 | 68 | |
| Semantic Textual Similarity | SemEval-2022 Task 2 Idiomatic STS (evaluation) | Spearman Rho (All)0.6613 | 14 | |
| Semantic Textual Similarity | STS English (test) | Spearman's ρ76.9 | 9 | |
| MWE Paraphrasing | English MWE SemEval (test) | P@110.8 | 9 | |
| Semantic Textual Similarity | SemEval STS Portuguese (PT) | Overall Score73.97 | 3 | |
| Semantic Textual Similarity | SemEval STS Galician (GL) | MWE Score34.74 | 3 |
Showing 6 of 6 rows