Unsupervised Paraphrasing of Multiword Expressions

Autor:	Wada, Takashi, Matsumoto, Yuji, Baldwin, Timothy, Lau, Jey Han
Rok vydání:	2023
Předmět:	Computer Science - Computation and Language Computer Science - Artificial Intelligence Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: 13 pages; accepted for Findings of ACL 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2306.01443 Zobrazit plný text záznamu View this record from Arxiv