In this paper, we present our approach for the "Nuanced Arabic Dialect
Identification (NADI) Shared Task 2023". We highlight our methodology for
subtask 1 which deals with country-level dialect identification. Recognizing
dialects plays an instrumental role in enhancing the performance of various
downstream NLP tasks such as speech recognition and translation. The task uses
the Twitter dataset (TWT-2023) that encompasses 18 dialects for the multi-class
classification problem. Numerous transformer-based models, pre-trained on
Arabic language, are employed for identifying country-level dialects. We
fine-tune these state-of-the-art models on the provided dataset. The ensembling
method is leveraged to yield improved performance of the system. We achieved an
F1-score of 76.65 (11th rank on the leaderboard) on the test dataset.

该研究提出了对 “Nuanced Arabic Dialect Identification (NADI) Shared Task 2023” 的方法，着重介绍了处理与国家级方言识别相关的子任务 1 的方法。该研究利用使用涵盖 18 种方言的 Twitter 数据集（TWT-2023）进行多类别分类问题的研究，采用基于 transformer 的预训练模型进行国家级方言识别，并利用集成方法提高了系统的性能，在测试数据集中取得了 76.65 的 F1 得分（排名第 11 位）。

2023 年 NADI 共享任务中的 Mavericks：使用基于 Transformer 的方法解开方言辨识中的区域细微差别

Mavericks at NADI 2023 Shared Task: Unravelling Regional Nuances through  Dialect Identification using Transformer-based Approach

We describe the findings of the fourth Nuanced Arabic Dialect Identification
Shared Task (NADI 2023). The objective of NADI is to help advance
state-of-the-art Arabic NLP by creating opportunities for teams of researchers
to collaboratively compete under standardized conditions. It does so with a
focus on Arabic dialects, offering novel datasets and defining subtasks that
allow for meaningful comparisons between different approaches. NADI 2023
targeted both dialect identification (Subtask 1) and dialect-to-MSA machine
translation (Subtask 2 and Subtask 3). A total of 58 unique teams registered
for the shared task, of whom 18 teams have participated (with 76 valid
submissions during test phase). Among these, 16 teams participated in Subtask
1, 5 participated in Subtask 2, and 3 participated in Subtask 3. The winning
teams achieved 87.27
F1 on Subtask 1, 14.76 Bleu in Subtask 2, and 21.10 Bleu in Subtask 3,
respectively. Results show that all three subtasks remain challenging, thereby
motivating future work in this area. We describe the methods employed by the
participating teams and briefly offer an outlook for NADI.

第四届 Nuanced Arabic Dialect Identification 共享任务（NADI 2023）的研究结果是：通过提供新颖的数据集和定义有意义的子任务，NADI 旨在推动最先进的阿拉伯语自然语言处理研究，并在标准化条件下促进团队合作和比较不同方法。该研究目标包括方言识别和方言到 MSA 机器翻译，参与的团队中获胜者在这些子任务中表现出色。结果表明这些子任务仍然具有挑战性，有助于推动未来相关工作的进行。

NADI 2023：第四届阿拉伯语细分方言识别共享任务

NADI 2023: The Fourth Nuanced Arabic Dialect Identification Shared Task

In this paper, we tackle the Nuanced Arabic Dialect Identification (NADI)
shared task (Abdul-Mageed et al., 2021) and demonstrate state-of-the-art
results on all of its four subtasks. Tasks are to identify the geographic
origin of short Dialectal (DA) and Modern Standard Arabic (MSA) utterances at
the levels of both country and province. Our final model is an ensemble of
variants built on top of MARBERT that achieves an F1-score of 34.03% for DA at
the country-level development set -- an improvement of 7.63% from previous
work.

本研究用 MARBERT 建立了一个 ensemble 模型来对短 DA 和 MSA 阿拉伯口语进行地理起源的识别，并在 4 个子任务上展现了最新的研究成果，进一步提高了之前工作 F1 评分约 7.63%。