在传播仇恨言论中解码虚假叙述：一种带有多任务学习的双头RoBERTa模型
Decoding Fake Narratives in Spreading Hateful Stories: A Dual-Head RoBERTa Model with Multi-Task Learning
AI总结：
本文提出一种双头RoBERTa模型，通过多任务学习检测社交媒体中的虚假叙述驱动的仇恨言论，并实现目标和严重性预测。
摘要：
社交媒体平台虽然促进了全球连接，但已成为有害内容快速传播的枢纽，包括仇恨言论和虚假叙述[Davidson2017, Shu2017]。Faux-Hate共享任务聚焦于检测一种特定现象：由虚假叙述驱动的仇恨言论生成，称为Faux-Hate。参与者被挑战在混合印地语-英语的社交媒体文本中识别此类实例。本文描述了我们为该共享任务开发的系统，解决两个主要子任务：(a)二元Faux-Hate检测，涉及虚假和仇恨言论分类，以及(b)目标和严重性预测，对仇恨内容的目标和严重性进行分类。我们的方法结合了先进的自然语言处理技术与领域特定的预训练，以提高两项任务的性能。系统取得了有竞争力的结果，证明了利用多任务学习解决这一复杂问题的有效性。
英文摘要：
Social media platforms, while enabling global connectivity, have become hubs for the rapid spread of harmful content, including hate speech and fake narratives \cite{davidson2017automated, shu2017fake}. The Faux-Hate shared task focuses on detecting a specific phenomenon: the generation of hate speech driven by fake narratives, termed Faux-Hate. Participants are challenged to identify such instances in code-mixed Hindi-English social media text. This paper describes our system developed for the shared task, addressing two primary sub-tasks: (a) Binary Faux-Hate detection, involving fake and hate speech classification, and (b) Target and Severity prediction, categorizing the intended target and severity of hateful content. Our approach combines advanced natural language processing techniques with domain-specific pretraining to enhance performance across both tasks. The system achieved competitive results, demonstrating the efficacy of leveraging multi-task learning for this complex problem.
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。
