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English(EN) An Explainable System for Offensive Content Detection in Nastaliq Urdu: Corpus Curation, Transformer Benchmarking, and Word-Level Attribution

新系统以87%的准确率检测冒犯性Nastaliq乌尔都语内容

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一个用于检测Nastaliq乌尔都语冒犯性内容的解释性系统，该脚本主要在Facebook等平台使用。该研究涉及构建一个包含7600多条评论的新语料库，微调了四个支持乌尔都语的Transformer模型，并将其性能与基线分类器进行了比较。表现最好的模型达到了87.05%的准确率，显著优于基线模型。使用词级SHAP归因进行的进一步分析显示，虽然性能部分依赖于识别诽谤性词语，但也包含了功能词和政治词汇，这表明对冒犯性的理解是复杂的。 AI

影响 提高了AI在资源匮乏语言中审核内容的能力，可能实现更广泛的全球平台安全。

排序理由 学术论文，详细介绍了针对特定语言和脚本的新语料库、模型基准测试和可解释性技术。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新系统以87%的准确率检测冒犯性Nastaliq乌尔都语内容

本文如何被排名

Signal score
17 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Tool
学术论文，详细介绍了针对特定语言和脚本的新语料库、模型基准测试和可解释性技术。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration
Single-source cluster
Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics
paper, safety
Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance
High
Clearly on-topic for AI-industry coverage.
Story freshness
Breaking (< 6h)
Fresh story with cross-source coverage still developing. Ranking may shift as more sources report.

完整方法见我们的编辑标准。

报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Muhammad Shahid Iqbal Malik ·

    一种用于检测Nastaliq乌尔都语冒犯性内容的解释性系统：语料库整理、Transformer基准测试和词级归因

    arXiv:2610.15795v1 Announce Type: cross Abstract: Automated moderation systems are deployed at scale, yet they remain opaque to the users and moderators affected by their decisions, and their coverage is almost entirely confined to English. For Urdu written in the Nastaliq script…

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