精细阿拉伯语方言识别共享任务的iCompass工作笔记

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

摘要

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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
iCompass Working Notes for the Nuanced Arabic Dialect Identification Shared task
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.
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