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جيل عنوان الإعلان باستخدام نموذج لغة ملثم ذاتي
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بالنسبة لأي موقع على شبكة الإنترنت للتجارة الإلكترونية، فهذا مشكلة غير خيالية تبني الإعلانات الدائمة التي تجذب المتسوقين.من الصعب اجتياز شريط الجودة الإبداعي للموقع، خاصة على نطاق واسع.وبالتالي نقترح حل برنامجي لتوليد عناوين إعلانات المنتج باستخدام محتوى البيع بالتجزئة.نقترح حالة من التطبيقات الفنية لطرق التدرج في سياسة التعلم (RL) على المحولات (Vaswani et al.، 2017) نماذج لغة ملثم مقرها (ديفلين وآخرون، 2019).تقوم طريقةنا بإنشاء العنوان الإعلاني من خلال تكييف مشترك على منتجات متعددة يرغب البائع في الإعلان.نوضح أن أسلوبنا تتفوق على أساليب المحولات الحالية و LSTM + RL في مقاييس تداخل وتدقيق الجودة.نظهر أيضا أن عناويننا النموذجية التي تم إنشاؤها تفوقت عناوين حقوق الإنسان المقدمة من حيث القواعد الناقدية والجودة الإبداعية على النحو المحدد بالتدقيق.
For any E-commerce website it is a nontrivial problem to build enduring advertisements that attract shoppers. It is hard to pass the creative quality bar of the website, especially at a large scale. We thus propose a programmatic solution to generate product advertising headlines using retail content. We propose a state of the art application of Reinforcement Learning (RL) Policy gradient methods on Transformer (Vaswani et al., 2017) based Masked Language Models (Devlin et al., 2019). Our method creates the advertising headline by jointly conditioning on multiple products that a seller wishes to advertise. We demonstrate that our method outperforms existing Transformer and LSTM + RL methods in overlap metrics and quality audits. We also show that our model generated headlines outperform human submitted headlines in terms of both grammar and creative quality as determined by audits.
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