SemEval-2022 Task 2: Multilingual Idiomaticity Detection and Sentence Embedding

Harish Tayyar Madabushi, Edward Gow-Smith, Marcos Garcia, Carolina Scarton, Marco Idiart, Aline Villavicencio

Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

25 Citations (SciVal)


This paper presents the shared task on Multilingual Idiomaticity Detection and Sentence Embedding, which consists of two subtasks: (a) a binary classification task aimed at identifying whether a sentence contains an idiomatic expression, and (b) a task based on semantic text similarity which requires the model to adequately represent potentially idiomatic expressions in context. Each subtask includes different settings regarding the amount of training data. Besides the task description, this paper introduces the datasets in English, Portuguese, and Galician and their annotation procedure, the evaluation metrics, and a summary of the participant systems and their results. The task had close to 100 registered participants organised into twenty five teams making over 650 and 150 submissions in the practice and evaluation phases respectively.
Original languageEnglish
Title of host publicationProceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
Place of PublicationSeattle, United States
PublisherAssociation for Computational Linguistics
Number of pages15
Publication statusPublished - 1 Jul 2022


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