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Register a new userPersonalized Response Generation with Tensor Factorization
توليد الاستجابة الشخصية مع عامل تونر
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يعد توليد الاستجابة الشخصية ضروريا لمزيد من المحادثات التي يشبها الإنسان. ومع ذلك، وكيفية نموذج معلومات تخصيص المستخدم مع عدم وجود أوصاف شخص مستخدم صريح أو التركيبة السكانية لا يزال قيد التحقيق فيها. لمعالجة مشكلة بيانات Sparsity للبيانات والعدد الهائل من المستخدمين، نستخدم عامل تخصيص Tensor لنموذج معلومات تخصيص المستخدمين مع تاريخ النشر. على وجه التحديد، نقدم تضمين الاستجابة الشخصية لجميع أزواج المستخدمين على المستخدمين وتشكيلها في موتر ثلاثي الحجم، متحللة من تحلل tucker. يتم تغذية تضمين الاستجابة الشخصية إما لمعرفة وحدة فك ترميز نموذج SEQ2SEQ القائمة على LSTM أو نموذج لغة محول للمساعدة في توليد المزيد من الردود الشخصية. لتقييم مدى تخصيص الاستجابات التي تم إنشاؤها، فإننا نقترح مزيدا من المرتبة المائية المستندة إلى الترتيب الواحد لكل من الزيارات @ k والتي تقيس أكثر من المرجح أن تأتي الردود التي تم إنشاؤها من المستخدمين المقابلين. تظهر النتائج على مجموعة بيانات المحادثة على نطاق واسع أن النماذج التي تعتمد على عامل توزيع العمال المقترح لدينا تولد استجابات أكثر تخصيصا وأكثر جودة مقارنة مع خطوط الأساس.
Personalized response generation is essential for more human-like conversations. However, how to model user personalization information with no explicit user persona descriptions or demographics still remains under-investigated. To tackle the data sparsity problem and the huge number of users, we utilize tensor factorization to model users' personalization information with their posting histories. Specifically, we introduce the personalized response embedding for all question-user pairs and form them into a three-mode tensor, decomposed by Tucker decomposition. The personalized response embedding is fed to either the decoder of an LSTM-based Seq2Seq model or a transformer language model to help generate more personalized responses. To evaluate how personalized the generated responses are, we further propose a novel ranking-based metric called Per-Hits@k which measures how likely are the generated responses come from the corresponding users. Results on a large-scale conversation dataset show that our proposed tensor factorization based models generate more personalized and higher quality responses compared to baselines.
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