Offensive language detection has been well studied in many languages, but it
is lagging behind in low-resource languages, such as Hebrew. In this paper, we
present a new offensive language corpus in Hebrew. A total of 15,881 tweets
were retrieved from Twitter. Each was labeled with one or more of five classes
(abusive, hate, violence, pornographic, or none offensive) by Arabic-Hebrew
bilingual speakers. The annotation process was challenging as each annotator is
expected to be familiar with the Israeli culture, politics, and practices to
understand the context of each tweet. We fine-tuned two Hebrew BERT models,
HeBERT and AlephBERT, using our proposed dataset and another published dataset.
We observed that our data boosts HeBERT performance by 2% when combined with
D_OLaH. Fine-tuning AlephBERT on our data and testing on D_OLaH yields 69%
accuracy, while fine-tuning on D_OLaH and testing on our data yields 57%
accuracy, which may be an indication to the generalizability our data offers.
Our dataset and fine-tuned models are available on GitHub and Huggingface.

在本研究中，我们提供了希伯来语的新冒犯性语言语料库，使用推特上的 15,881 条推文，并由阿拉伯 - 希伯来双语人士对其进行了五个类别（辱骂、仇恨、暴力、色情或无冒犯）的标注，我们使用我们提出的数据集和另一个已发布的数据集对两个希伯来语 BERT 模型（HeBERT 和 AlephBERT）进行了微调，观察到我们的数据与 D_OLaH 相结合可以将 HeBERT 的性能提升 2％。将 AlephBERT 在我们的数据上进行微调并在 D_OLaH 上进行测试，准确率达到了 69％，而在 D_OLaH 上进行微调并在我们的数据上进行测试，准确率达到了 57％，这可能意味着我们提供的数据的通用性。我们的数据集和微调模型已在 GitHub 和 Huggingface 上提供。

使用 BERT 的希伯来语冒犯语料库和检测

Offensive Hebrew Corpus and Detection using BERT

Large Pre-trained Language Models (PLMs) have become ubiquitous in the
development of language understanding technology and lie at the heart of many
artificial intelligence advances. While advances reported for English using
PLMs are unprecedented, reported advances using PLMs in Hebrew are few and far
between. The problem is twofold. First, Hebrew resources available for training
NLP models are not at the same order of magnitude as their English
counterparts. Second, there are no accepted tasks and benchmarks to evaluate
the progress of Hebrew PLMs on. In this work we aim to remedy both aspects.
First, we present AlephBERT, a large pre-trained language model for Modern
Hebrew, which is trained on larger vocabulary and a larger dataset than any
Hebrew PLM before. Second, using AlephBERT we present new state-of-the-art
results on multiple Hebrew tasks and benchmarks, including: Segmentation,
Part-of-Speech Tagging, full Morphological Tagging, Named-Entity Recognition
and Sentiment Analysis. We make our AlephBERT model publicly available,
providing a single point of entry for the development of Hebrew NLP
applications.

本文介绍了 AlephBERT，一个用于现代希伯来语的大型预训练语言模型，使用该模型在多个希伯来语任务和基准测试中提供了新的最先进结果，包括分词、词性标注、完整的形态标注、命名实体识别和情感分析，并公开了模型。