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.
2022, Findings of the Association for Computational Linguistics: NAACL 2022, Pages 2715-2726
ID10M: Idiom Identification in 10 Languages (04b Atto di convegno in volume)
Tedeschi Simone, Martelli Federico, Navigli Roberto
Gruppo di ricerca: Natural Language Processing