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Register a new userWIKIBIAS: Detecting Multi-Span Subjective Biases in Language
Wikibias: الكشف عن التحيزات الذاتية متعددة الأجزاء في اللغة
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لا تزال التحيزات منتشرة في النصوص والإعلام الحديث، وخاصة التحيز الذاتي - نوع خاص من التحيز الذي يقدم مواقف غير لائقة أو يقدم بيان مع افتراض الحقيقة. لمعالجة مشكلة الكشف عن التحيز الذاتي والتخفيف، نقدم كوربايا موازية مشروح يدويا مع أكثر من 4000 زوجا من التعديلات في ويكيبيديا. تحتوي هذه الجوربوس على شروح تجاه كل من أنواع التحيز على مستوى الجملة وشرائح منحازة على مستوى الرمز المميز. نقدم تحليلات منهجية لمجموعة بياناتنا والنتائج التي حققتها مجموعة من خطوط الأساس الحديثة من حيث ثلاث مهام: تصنيف التحيز، ووضع علامات شرائح منحازة، وتحييد النص المتحيز. نجد أن النماذج الحالية لا تزال تكافح مع اكتشاف التحيزات متعددة الأجزاء على الرغم من أدائها المعقول، مما يشير إلى أن DataSet لدينا يمكن أن تكون بمثابة معيار بحث مفيد. نوضح أيضا أن النماذج المدربة على DataSet الخاصة بنا يمكن أن تعزز جيدا إلى مجالات متعددة مثل الأخبار والخطب السياسية.
Biases continue to be prevalent in modern text and media, especially subjective bias -- a special type of bias that introduces improper attitudes or presents a statement with the presupposition of truth. To tackle the problem of detecting and further mitigating subjective bias, we introduce a manually annotated parallel corpus WIKIBIAS with more than 4,000 sentence pairs from Wikipedia edits. This corpus contains annotations towards both sentence-level bias types and token-level biased segments. We present systematic analyses of our dataset and results achieved by a set of state-of-the-art baselines in terms of three tasks: bias classification, tagging biased segments, and neutralizing biased text. We find that current models still struggle with detecting multi-span biases despite their reasonable performances, suggesting that our dataset can serve as a useful research benchmark. We also demonstrate that models trained on our dataset can generalize well to multiple domains such as news and political speeches.
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