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

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arxiv 2204.10050 v2 pith:3RWMCEA7 submitted 2022-04-21 cs.CL

Harish Tayyar Madabushi , Edward Gow-Smith , Marcos Garcia , Carolina Scarton , Marco Idiart , Aline Villavicencio This is my paper · ORCID
classification cs.CL
keywords tasksentencedetectionembeddingevaluationidiomaticidiomaticitymultilingual
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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.

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  1. Stands to Reason: Investigating the Effect of Reasoning on Idiomaticity Detection

    cs.CL 2025-08 conditional novelty 6.0 of 10

    Reasoning-tuned DeepSeek models give only small, inconsistent gains over base models on idiomaticity detection, and supplying definitions from large models can improve smaller models on some datasets.

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