This paper reports the findings of the ICON 2023 on Gendered Abuse Detection
in Indic Languages. The shared task deals with the detection of gendered abuse
in online text. The shared task was conducted as a part of ICON 2023, based on
a novel dataset in Hindi, Tamil and the Indian dialect of English. The
participants were given three subtasks with the train dataset consisting of
approximately 6500 posts sourced from Twitter. For the test set, approximately
1200 posts were provided. The shared task received a total of 9 registrations.
The best F-1 scores are 0.616 for subtask 1, 0.572 for subtask 2 and, 0.616 and
0.582 for subtask 3. The paper contains examples of hateful content owing to
its topic.

在 ICON 2023 上报告了有关 Indic 语言中的性别虐待检测的研究结果，该共享任务涉及在在线文本中检测性别虐待，使用了新的数据集：印地语、泰米尔语和印度英语方言。

2023 年 ICON 共享任务概述：印度语中性别虐待检测

Overview of the 2023 ICON Shared Task on Gendered Abuse Detection in  Indic Languages

We study Socially Unacceptable Discourse (SUD) characterization and detection
in online text. We first build and present a novel corpus that contains a large
variety of manually annotated texts from different online sources used so far
in state-of-the-art Machine learning (ML) SUD detection solutions. This global
context allows us to test the generalization ability of SUD classifiers that
acquire knowledge around the same SUD categories, but from different contexts.
From this perspective, we can analyze how (possibly) different annotation
modalities influence SUD learning by discussing open challenges and open
research directions. We also provide several data insights which can support
domain experts in the annotation task.

研究网络文本中的社交不可接受辞谢（SUD）的特征与检测，提出了一个新颖的包含不同在线来源手动标注文本的语料库，并通过分析开放挑战和研究方向讨论可能不同注释方式对 SUD 学习的影响，以此来测试 SUD 分类器的泛化能力。同时，提供了一些数据洞察，以支持领域专家进行注释任务。