发表机构
University of Sydney（悉尼大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对游戏内毒性检测中短文本与俚语挑战，提出带注意力残差的双向表示模型BRAR，在槽填充任务上优于基线。
AI 中文摘要
游戏内毒性语言已成为游戏行业和社区中一个关键关注点。尽管已有多个用于在线游戏毒性分析的框架和模型被提出，但在玩家聊天话语中检测毒性仍然是一项艰巨的挑战：这不仅源于此类话语极短的篇幅，还因为其高度依赖游戏俚语、缩写和领域特定行话，而通用语言模型难以识别这些内容。本文提出了一个基于真实游戏内聊天数据的游戏内毒性语言检测共享任务，并提出了在毒性语言槽填充任务上表现最佳的模型：带注意力残差的双向表示（BRAR）。实验结果表明，BRAR能够有效捕捉全局上下文，并在槽填充任务上优于现有基线模型。
英文摘要
In-game toxic language has emerged as a critical concern in the gaming industry and community. While several frameworks and models for online game toxicity analysis have been proposed, detecting toxicity in player chat utterances remains a formidable challenge: stemming not only from the extremely short length of such utterances but also from the heavy reliance on game slang, abbreviations, and domain-specific jargon, which generic language models are poorly suited to recognize. This paper presents a shared task for in-game toxic language detection built upon real-world in-game chat data, and proposes the best-preforming model for the toxic language slot filling: Bi-directional Representations with Attention Residuals (BRAR). Experimental results demonstrate that BRAR effectively captures the global context and outperforms the existing baselines on slot filling.
CommentsAccepted by AAAI 2023
