论文标题

Semeval-2022任务2:多语言惯用性检测和句子嵌入

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

论文作者

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

论文摘要

本文介绍了多语言惯用性检测和句子嵌入的共同任务,该任务由两个子任务组成:(a)旨在识别句子是否包含惯用性表达的二进制分类任务,以及(b)基于语义文本相似性的任务,该任务需要在上下文中充分代表模型的模型。每个子任务包括有关培训数据量的不同设置。除任务说明外,本文还介绍了英语,葡萄牙语和加利西亚语的数据集及其注释程序,评估指标以及参与者系统及其结果的摘要。该任务分别在25个团队中组织了近100名注册参与者,分别在实践和评估阶段进行了650多个参与者。

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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