论文标题
CS-UM6P在Semeval-2022任务6：基于变压器的模型，用于英语和阿拉伯语中的讽刺检测
CS-UM6P at SemEval-2022 Task 6: Transformer-based Models for Intended Sarcasm Detection in English and Arabic
论文作者
论文摘要
讽刺是一种形式的形式，其中句子的预期含义与其字面意义不同。这对几种自然语言处理（NLP）应用（例如情感分析，意见挖掘和作者概况）构成了严重的挑战。在本文中，我们将参与系统介绍给了英语和阿拉伯语的预期讽刺检测任务。我们的系统\ footNote {我们系统的源代码可在\ url {https://github.com/abdelkadermh/isarcasmeval}}}中获得，该模型由三个基于深度学习的模型组成，利用了两种现有的用于阿拉伯语和英语的现有预培养的语言。我们参加了所有子任务。我们的官方意见在阿拉伯语的子任务A上取得了最佳表现，在子任务B中排名第二。对于子任务C，我们的系统分别在阿拉伯语和英语数据集上排名第7和第11位。
Sarcasm is a form of figurative language where the intended meaning of a sentence differs from its literal meaning. This poses a serious challenge to several Natural Language Processing (NLP) applications such as Sentiment Analysis, Opinion Mining, and Author Profiling. In this paper, we present our participating system to the intended sarcasm detection task in English and Arabic languages. Our system\footnote{The source code of our system is available at \url{https://github.com/AbdelkaderMH/iSarcasmEval}} consists of three deep learning-based models leveraging two existing pre-trained language models for Arabic and English. We have participated in all sub-tasks. Our official submissions achieve the best performance on sub-task A for Arabic language and rank second in sub-task B. For sub-task C, our system is ranked 7th and 11th on Arabic and English datasets, respectively.
