تحديد ما إذا كان هناك مستندان مؤلفان من المؤلف نفسه، المعروف أيضا باسم التحقق من التأليف، تم تناوله تقليديا باستخدام الأساليب الإحصائية. في الآونة الأخيرة، تم العثور على تمثيلات التأليف المستفادة باستخدام الشبكات العصبية لتفوق البدائل، لا سيما في الإعدادات الكبيرة التي تنطوي على مئات الآلاف من المؤلفين. ولكن هل تعلمت هذه التمثيلات في نقل مجال معين إلى مجالات أخرى؟ أو هل هذه تمثيلات متشابكة بطبيعتها مع ميزات خاصة بالمجال؟ لدراسة هذه الأسئلة، نقوم بإجراء أول دراسة واسعة النطاق لنقل المجال المتبادل للتحقق من التأليف بالنظر إلى التحويلات الصفرية التي تنطوي على ثلاثة مجالات متباينة: مراجعات الأمازون وقصص قصيرة الصوفية والتعليقات Reddit. نجد أنه على الرغم من أن درجة التنقل مفاجأة ممكنة بين مجالات معينة، إلا أنها ليست ناجحة بين الآخرين. نحن ندرس خصائص هذه المجالات التي تؤثر على التعميم واقتراح أساليب بسيطة ولكنها فعالة لتحسين النقل.
Determining whether two documents were composed by the same author, also known as authorship verification, has traditionally been tackled using statistical methods. Recently, authorship representations learned using neural networks have been found to outperform alternatives, particularly in large-scale settings involving hundreds of thousands of authors. But do such representations learned in a particular domain transfer to other domains? Or are these representations inherently entangled with domain-specific features? To study these questions, we conduct the first large-scale study of cross-domain transfer for authorship verification considering zero-shot transfers involving three disparate domains: Amazon reviews, fanfiction short stories, and Reddit comments. We find that although a surprising degree of transfer is possible between certain domains, it is not so successful between others. We examine properties of these domains that influence generalization and propose simple but effective methods to improve transfer.
References used
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