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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 one 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. ... : Data available at https://github.com/H-TayyarMadabushi/SemEval_2022_Task2-idiomaticity and competition website at https://sites.google.com/view/semeval2022task2-idiomaticity ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://arxiv.org/abs/2204.10050
https://dx.doi.org/10.48550/arxiv.2204.10050
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2
Improving Tokenisation by Alternative Treatment of Spaces ...
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3
Unsupervised Word Segmentation from Discrete Speech Units in Low-Resource Settings ...
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4
Investigating Language Impact in Bilingual Approaches for Computational Language Documentation ...
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5
How Does Language Influence Documentation Workflow? Unsupervised Word Discovery Using Translations in Multiple Languages ...
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6
Unsupervised Word Segmentation from Speech with Attention ...
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7
Unwritten Languages Demand Attention Too! Word Discovery with Encoder-Decoder Models ...
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