PAN23 Trigger Detection

This is the dataset for the shared task on Trigger Detection at PAN@CLEF2023. Please consult the task's page for further details on the format, the dataset's creation, and links to baselines and utility code. You can find a more refined version of this work here: github.com/webis-de/ACL-23. Task: In trigger detection, we want to assign trigger warning labels to documents that contain potentially discomforting or distressing (triggering) content. We model trigger detection as a multi-label document classification challenge: assign each document all appropriate trigger warnings, but not more. All warnings are chosen from the author's perspective, i.e. the work's author decided which kind of trigger the document contains. Dataset: This dataset contains annotated works of fanfiction, extracted from archiveofourown.org (AO3). Each work is between 50 and 6,000 words long and has between 1 and many trigger warnings assigned. Our training dataset contains 307,102 examples, with 17,104 in validation and 17,040 in the test split. The label set contains 32 different trigger warnings. All labels are based on the freeform content warnings added to a fanwork by its author. Versioning: 1.0: initial upload 1.1 fixed a minor bug where some works in the labels.jsonl contained labels that are not used in the competition (heteronormativity and religious-discrimination). Those labels have been removed. 1.2 added labels.jsonl for the test dataset.本数据集为PAN@CLEF2023触发检测共享任务专属数据集。如需了解数据集格式、构建方式以及基线模型与工具代码的相关链接，请参阅该任务的官方页面。 你可通过以下链接获取本工作的优化版本：github.com/webis-de/ACL-23。 任务说明：在触发检测任务中，我们需要为包含潜在不适或令人痛苦的（触发型）内容的文档分配触发警告标签。我们将触发检测建模为多标签文档分类任务：为每份文档分配所有恰当的触发警告，但不得过度标注。所有警告均以作品作者的视角进行标注，即由作品作者自行判定文档包含何种类型的触发内容。 数据集概况：本数据集包含从archiveofourown.org（AO3）提取的标注同人作品。每份作品的字数介于50至6000字之间，且被分配了1个及以上的触发警告标签。训练集共包含307102条样本，验证集与测试集分别包含17104条与17040条样本。标签集合涵盖32种不同的触发警告，所有标签均基于作品作者为同人作品添加的自由形式内容警告（freeform content warnings）。 版本更新： 1.0：初始上传版本 1.1：修复一处微小缺陷：原labels.jsonl文件中部分作品包含大赛未使用的标签（异性恋中心主义（heteronormativity）与宗教歧视（religious-discrimination）），现已移除这些无效标签。 1.2：为测试集新增labels.jsonl文件。