Abstract
Idioms are phrases which present a figurative meaning that cannot be (completely) derived by looking at the meaning of their individual components. Identifying and understanding idioms in context is a crucial goal and a key challenge in a wide range of Natural Language Understanding tasks. Although efforts have been undertaken in this direction, the automatic identification and understanding of idioms is still a largely under-investigated area, especially when operating in a multilingual scenario. In this paper, we address such limitations and put forward several new contributions: we propose a novel multilingual Transformer-based system for the identification of idioms; we produce a high-quality automatically-created training dataset in 10 languages, along with a novel manually-curated evaluation benchmark; finally, we carry out a thorough performance analysis and release our evaluation suite at https://github.com/Babelscape/ID10M.- Anthology ID:
- 2022.findings-naacl.208
- Volume:
- Findings of the Association for Computational Linguistics: NAACL 2022
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Editors:
- Marine Carpuat, Marie-Catherine de Marneffe, Ivan Vladimir Meza Ruiz
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2715–2726
- Language:
- URL:
- https://aclanthology.org/2022.findings-naacl.208
- DOI:
- 10.18653/v1/2022.findings-naacl.208
- Cite (ACL):
- Simone Tedeschi, Federico Martelli, and Roberto Navigli. 2022. ID10M: Idiom Identification in 10 Languages. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 2715–2726, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- ID10M: Idiom Identification in 10 Languages (Tedeschi et al., Findings 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2022.findings-naacl.208.pdf
- Code
- babelscape/id10m
- Data
- WikiMatrix