AraBERT and Farasa Segmentation Based Approach For Sarcasm and Sentiment Detection in Arabic Tweets

Dialect Identification in Nuanced Arabic Tweets Using Farasa Segmentation and AraBERT

This paper presents our approach to address the EACL WANLP-2021 Shared Task 1: Nuanced Arabic Dialect Identification (NADI). The task is aimed at developing a system that identifies the geographical location(country/province) from where an Arabic tweet in the form of modern standard Arabic or dialect comes from. We solve the task in two parts. The first part involves pre-processing the provided dataset by cleaning, adding and segmenting various parts of the text. This is followed by carrying out experiments with different versions of two Transformer based models, AraBERT and AraELECTRA. Our fi…

表情符号排除对阿拉伯语讽刺检测模型性能的影响

Impact of emoji exclusion on the performance of Arabic sarcasm detection models

本文研究从数据集中排除表情符号对阿拉伯语社交媒体讽刺检测模型性能的影响，通过适配和增强AraBERT预训练模型证明移除表情符号可显著提升检测准确率，并为阿拉伯语自然语言处理建立新基准。

Combining Context-Free and Contextualized Representations for Arabic Sarcasm Detection and Sentiment Identification

Since their inception, transformer-based language models have led to impressive performance gains across multiple natural language processing tasks. For Arabic, the current state-of-the-art results on most datasets are achieved by the AraBERT language model. Notwithstanding these recent advancements, sarcasm and sentiment detection persist to be challenging tasks in Arabic, given the language's rich morphology, linguistic disparity and dialectal variations. This paper proffers team SPPU-AASM's submission for the WANLP ArSarcasm shared-task 2021, which centers around the sarcasm and sentiment p…