iCompass Working Notes for the Nuanced Arabic Dialect Identification Shared task

Abir Messaoudi, Chayma Fourati, H. Haddad, Moez BenHajhmida
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引用次数: 4

Abstract

We describe our submitted system to the Nuanced Arabic Dialect Identification (NADI) shared task. We tackled only the first subtask (Subtask 1). We used state-of-the-art Deep Learning models and pre-trained contextualized text representation models that we finetuned according to the downstream task in hand. As a first approach, we used BERT Arabic variants: MARBERT with its two versions MARBERT v1 and MARBERT v2, we combined MARBERT embeddings with a CNN classifier, and finally, we tested the Quasi-Recurrent Neural Networks (QRNN) model. The results found show that version 2 of MARBERT outperforms all of the previously mentioned models on Subtask 1.
精细阿拉伯语方言识别共享任务的iCompass工作笔记
我们将提交的系统描述为细微差别阿拉伯方言识别(NADI)共享任务。我们只处理了第一个子任务(子任务1)。我们使用了最先进的深度学习模型和预训练的上下文文本表示模型,我们根据手头的下游任务进行了微调。作为第一种方法,我们使用了BERT阿拉伯语变体:MARBERT及其两个版本MARBERT v1和MARBERT v2,我们将MARBERT嵌入与CNN分类器相结合,最后,我们测试了准循环神经网络(QRNN)模型。结果表明,MARBERT的版本2在子任务1上优于前面提到的所有模型。
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