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。

ArBanking77：意图检测的神经模型与现代及辩证阿拉伯语言的新数据集

ArBanking77: Intent Detection Neural Model and a New Dataset in Modern  and Dialectical Arabic

We introduce a generic, language-independent method to collect a large
percentage of offensive and hate tweets regardless of their topics or genres.
We harness the extralinguistic information embedded in the emojis to collect a
large number of offensive tweets. We apply the proposed method on Arabic tweets
and compare it with English tweets - analysing key cultural differences. We
observed a constant usage of these emojis to represent offensiveness throughout
different timespans on Twitter. We manually annotate and publicly release the
largest Arabic dataset for offensive, fine-grained hate speech, vulgar and
violence content. Furthermore, we benchmark the dataset for detecting
offensiveness and hate speech using different transformer architectures and
perform in-depth linguistic analysis. We evaluate our models on external
datasets - a Twitter dataset collected using a completely different method, and
a multi-platform dataset containing comments from Twitter, YouTube and
Facebook, for assessing generalization capability. Competitive results on these
datasets suggest that the data collected using our method captures universal
characteristics of offensive language. Our findings also highlight the common
words used in offensive communications, common targets for hate speech,
specific patterns in violence tweets; and pinpoint common classification errors
that can be attributed to limitations of NLP models. We observe that even
state-of-the-art transformer models may fail to take into account culture,
background and context or understand nuances present in real-world data such as
sarcasm.

通过利用表情符号中的额外语言信息，我们提出了一种通用的、语言无关的收集攻击性和仇恨推文的方法，并在阿拉伯推文上应用和比较。我们手动注释并公开发布了最大的阿拉伯数据集，用于检测讽刺、粗俗和暴力内容，并针对不同的转换器架构和进行了深入的语言分析。我们的发现突出了攻击性通信中使用的常见单词、仇恨言论的常见目标、暴力推文中的特定模式，以及可能归因于 NLP 模型局限性的常见分类错误，并指出即使是最先进的 Transformer 模型在识别现实世界数据（如讽刺）中存在的文化、背景和语境的细微差异时也可能无法考虑到。