FIRE 2023 HASOC 子任务概述：通过跨度检测识别英语中构成显性仇恨的标记
Overview of the HASOC Subtrack at FIRE 2023: Identification of Tokens Contributing to Explicit Hate in English by Span Detection
AI总结：
本文概述了 FIRE 2023 的 HASOC 子任务，该任务通过跨度检测识别英语推文中构成显性仇恨的标记，共有12支队伍参赛，最高宏F1为0.58。
摘要：
随着仇恨言论在网络上的持续泛滥，开发计算方法来缓解这一问题变得越来越重要。被动地，使用黑盒模型识别仇恨内容可能会让用户困惑，不明白为什么他们的帖子被自动标记为仇恨内容。另一方面，主动缓解可以通过在帖子公开之前建议改写来实现。然而，这两种缓解技术都需要知道帖子中哪一部分包含仇恨方面，即文本中哪些跨度负责传达仇恨。更好地检测此类跨度可以显著减少网络上的显性仇恨内容。为了进一步推动这一研究领域，我们在 HASOC-FIRE 2023 上组织了 HateNorm，专注于英语推文中的显性跨度检测。共有 12 支队伍参加了比赛，观察到的最高宏 F1 分数为 0.58。
英文摘要：
As hate speech continues to proliferate on the web, it is becoming increasingly important to develop computational methods to mitigate it. Reactively, using black-box models to identify hateful content can perplex users as to why their posts were automatically flagged as hateful. On the other hand, proactive mitigation can be achieved by suggesting rephrasing before a post is made public. However, both mitigation techniques require information about which part of a post contains the hateful aspect, i.e., what spans within a text are responsible for conveying hate. Better detection of such spans can significantly reduce explicitly hateful content on the web. To further contribute to this research area, we organized HateNorm at HASOC-FIRE 2023, focusing on explicit span detection in English Tweets. A total of 12 teams participated in the competition, with the highest macro-F1 observed at 0.58.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
