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

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

doi:10.18653/v1/2022.semeval-1.13

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 proceeding › Chapter in a published conference proceeding

35 Citations (SciVal)

Abstract

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 language	English
Title of host publication	Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
Place of Publication	Seattle, United States
Publisher	Association for Computational Linguistics
Pages	107-121
Number of pages	15
DOIs	https://doi.org/10.18653/v1/2022.semeval-1.13
Publication status	Published - 1 Jul 2022

Access to Document

10.18653/v1/2022.semeval-1.13Licence: CC BY

https://aclanthology.org/2022.semeval-1.13Licence: CC BY

Cite this

Tayyar Madabushi, H., Gow-Smith, E., Garcia, M., Scarton, C., Idiart, M., & Villavicencio, A. (2022). SemEval-2022 Task 2: Multilingual Idiomaticity Detection and Sentence Embedding. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) (pp. 107-121). Article 2022.semeval-1.13 Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.semeval-1.13

SemEval-2022 Task 2: Multilingual Idiomaticity Detection and Sentence Embedding. / Tayyar Madabushi, Harish; Gow-Smith, Edward; Garcia, Marcos et al.
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022). Seattle, United States: Association for Computational Linguistics, 2022. p. 107-121 2022.semeval-1.13.

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

Tayyar Madabushi, H, Gow-Smith, E, Garcia, M, Scarton, C, Idiart, M & Villavicencio, A 2022, SemEval-2022 Task 2: Multilingual Idiomaticity Detection and Sentence Embedding. in Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)., 2022.semeval-1.13, Association for Computational Linguistics, Seattle, United States, pp. 107-121. https://doi.org/10.18653/v1/2022.semeval-1.13

Tayyar Madabushi H, Gow-Smith E, Garcia M, Scarton C, Idiart M, Villavicencio A. SemEval-2022 Task 2: Multilingual Idiomaticity Detection and Sentence Embedding. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022). Seattle, United States: Association for Computational Linguistics. 2022. p. 107-121. 2022.semeval-1.13 doi: 10.18653/v1/2022.semeval-1.13

@inproceedings{7f4696f7bb3c4427ac0a4ababbe272b3,

title = "SemEval-2022 Task 2: Multilingual Idiomaticity Detection and Sentence Embedding",

abstract = "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.",

author = "{Tayyar Madabushi}, Harish and Edward Gow-Smith and Marcos Garcia and Carolina Scarton and Marco Idiart and Aline Villavicencio",

year = "2022",

month = jul,

day = "1",

doi = "10.18653/v1/2022.semeval-1.13",

language = "English",

pages = "107--121",

booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",

publisher = "Association for Computational Linguistics",

}

TY - GEN

T1 - SemEval-2022 Task 2: Multilingual Idiomaticity Detection and Sentence Embedding

AU - Tayyar Madabushi, Harish

AU - Gow-Smith, Edward

AU - Garcia, Marcos

AU - Scarton, Carolina

AU - Idiart, Marco

AU - Villavicencio, Aline

PY - 2022/7/1

Y1 - 2022/7/1

N2 - 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.

AB - 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.

U2 - 10.18653/v1/2022.semeval-1.13

DO - 10.18653/v1/2022.semeval-1.13

M3 - Chapter in a published conference proceeding

SP - 107

EP - 121

BT - Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

PB - Association for Computational Linguistics

CY - Seattle, United States

ER -

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

Abstract

Access to Document

Fingerprint

Cite this