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Register a new userCollecting a Large-Scale Gender Bias Dataset for Coreference Resolution and Machine Translation
جمع مجموعة بيانات BIAS الجنسية على نطاق واسع لحل Aquerence والترجمة الآلية
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وقد وجدت الأعمال الحديثة دليلا على التحيز بين الجنسين في نماذج من الترجمة الآلية ودقة Aquerence باستخدام مجموعات بيانات التشخيص الاصطناعية في الغالب. في حين أن هذه التحيز الكمي في تجربة خاضعة للرقابة، فإنها غالبا ما تفعل ذلك على نطاق صغير وتتكون في معظمها من الجمل الاصطناعية، خارج التوزيع. في هذا العمل، نجد أنماط نحوية تشير إلى مهام الدورانية النمطية وغير النمطية (مثل الممرضات الإناث مقابل الراقصين الذكور) في كوربورا من ثلاثة مجالات، مما أدى إلى أول مجموعة بيانات BIAS الجنسية على نطاق واسع من 108 ألفا جمل. نحن نتحقق يدويا من جودة Corpus الخاصة بنا واستخدامها لتقييم التحيز بين الجنسين في نماذج تحليل الأسلحة الأساسية المختلفة ونماذج الترجمة الآلية. نجد أن جميع النماذج المختبرة تميل إلى الإفراط في الاعتماد على الصور النمطية الجنسانية عند تقديمها مع المدخلات الطبيعية، والتي قد تكون ضارة بشكل خاص عند نشرها في النظم التجارية. أخيرا، نظيرنا على أن مجموعة بياناتنا تضفي نفسها على نموذج دقة COMEARCASE، ويجد أن يجدد التحيز على مجموعة مشغولة. تتوفر DataSet ونماذجنا علنا في Github.com/slab-nlp/bug. نأمل أن يحفزون البحوث المستقبلية في تقنيات تخفيف تقييم التقييم بين الجنسين في الإعدادات الواقعية.
Recent works have found evidence of gender bias in models of machine translation and coreference resolution using mostly synthetic diagnostic datasets. While these quantify bias in a controlled experiment, they often do so on a small scale and consist mostly of artificial, out-of-distribution sentences. In this work, we find grammatical patterns indicating stereotypical and non-stereotypical gender-role assignments (e.g., female nurses versus male dancers) in corpora from three domains, resulting in a first large-scale gender bias dataset of 108K diverse real-world English sentences. We manually verify the quality of our corpus and use it to evaluate gender bias in various coreference resolution and machine translation models. We find that all tested models tend to over-rely on gender stereotypes when presented with natural inputs, which may be especially harmful when deployed in commercial systems. Finally, we show that our dataset lends itself to finetuning a coreference resolution model, finding it mitigates bias on a held out set. Our dataset and models are publicly available at github.com/SLAB-NLP/BUG. We hope they will spur future research into gender bias evaluation mitigation techniques in realistic settings.
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