Arabic is one of the most important and growing languages in the world. With
the rise of social media platforms such as Twitter, Arabic spoken dialects have
become more in use. In this paper, we describe our approach on the NADI Shared
Task 1 that requires us to build a system to differentiate between different 21
Arabic dialects, we introduce a deep learning semi-supervised fashion approach
along with pre-processing that was reported on NADI shared Task 1 Corpus. Our
system ranks 4th in NADI's shared task competition achieving a 23.09% F1 macro
average score with a simple yet efficient approach to differentiating between
21 Arabic Dialects given tweets.

本文介绍了使用深度学习和半监督的方法对不同的 21 个阿拉伯方言进行区分，并在 NADI 共享任务竞赛中排名第 4，其 F1 宏平均得分为 23.09%。

使用基于 BERT 的领域自适应进行阿拉伯方言识别

Arabic Dialect Identification Using BERT-Based Domain Adaptation

Arabic dialect identification is a complex problem for a number of inherent
properties of the language itself. In this paper, we present the experiments
conducted, and the models developed by our competing team, Mawdoo3 AI, along
the way to achieving our winning solution to subtask 1 of the Nuanced Arabic
Dialect Identification (NADI) shared task. The dialect identification subtask
provides 21,000 country-level labeled tweets covering all 21 Arab countries. An
unlabeled corpus of 10M tweets from the same domain is also presented by the
competition organizers for optional use. Our winning solution itself came in
the form of an ensemble of different training iterations of our pre-trained
BERT model, which achieved a micro-averaged F1-score of 26.78% on the subtask
at hand. We publicly release the pre-trained language model component of our
winning solution under the name of Multi-dialect-Arabic-BERT model, for any
interested researcher out there.

本文介绍了 Mawdoo3 AI 团队开发的预训练 BERT 模型的表现，以及该模型在 Nuanced Arabic Dialect Identification（NADI）共享任务的子任务 1 中获胜的过程和方法，并公开了 Multi-dialect-Arabic-BERT 模型的预训练语言模型组件，供其他研究人员使用。