This paper presents the ArBanking77, a large Arabic dataset for intent
detection in the banking domain. Our dataset was arabized and localized from
the original English Banking77 dataset, which consists of 13,083 queries to
ArBanking77 dataset with 31,404 queries in both Modern Standard Arabic (MSA)
and Palestinian dialect, with each query classified into one of the 77 classes
(intents). Furthermore, we present a neural model, based on AraBERT, fine-tuned
on ArBanking77, which achieved an F1-score of 0.9209 and 0.8995 on MSA and
Palestinian dialect, respectively. We performed extensive experimentation in
which we simulated low-resource settings, where the model is trained on a
subset of the data and augmented with noisy queries to simulate colloquial
terms, mistakes and misspellings found in real NLP systems, especially live
chat queries. The data and the models are publicly available at
this https URL

这篇论文介绍了 ArBanking77，这是一个在银行领域用于意图检测的大型阿拉伯语数据集。我们从原始的英文 Banking77 数据集进行了阿拉伯化和本土化处理，生成了 ArBanking77 数据集，其中包含了 31,404 个现代标准阿拉伯（MSA）和巴勒斯坦方言的查询，每个查询被分类为 77 个类别（意图）。此外，我们还提出了基于 AraBERT 的神经模型，经过在 ArBanking77 上微调后，在 MSA 和巴勒斯坦方言上分别获得了 0.9209 和 0.8995 的 F1 分数。我们进行了大量实验证明，在低资源环境下，该模型通过在部分数据上训练，并使用噪声查询进行扩充以模拟现实中自然语言处理系统（尤其是实时聊天查询）中的口语术语、错误和拼写错误。数据集和模型均可在此链接处公开获得。