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تعلم تمثيلات التأليف الشامل
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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.
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We study a new problem of cross-lingual transfer learning for event coreference resolution (ECR) where models trained on data from a source language are adapted for evaluations in different target languages. We introduce the first baseline model for
this task based on XLM-RoBERTa, a state-of-the-art multilingual pre-trained language model. We also explore language adversarial neural networks (LANN) that present language discriminators to distinguish texts from the source and target languages to improve the language generalization for ECR. In addition, we introduce two novel mechanisms to further enhance the general representation learning of LANN, featuring: (i) multi-view alignment to penalize cross coreference-label alignment of examples in the source and target languages, and (ii) optimal transport to select close examples in the source and target languages to provide better training signals for the language discriminators. Finally, we perform extensive experiments for cross-lingual ECR from English to Spanish and Chinese to demonstrate the effectiveness of the proposed methods.
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ials are available, problems remain which prevent researchers from comparing the effectiveness of different methods. To solve the remaining problems---the lack of fixed test sets and the use of inappropriately homogeneous corpora---our paper contributes materials for five closed-set authorship identification experiments. The five experiments feature texts from 106 distinct authors. Experiments involve a range of contemporary non-fiction American English prose. These experiments provide the foundation for comparable and reproducible authorship attribution research involving contemporary writing.
The design of expressive representations of entities and relations in a knowledge graph is an important endeavor. While many of the existing approaches have primarily focused on learning from relational patterns and structural information, the intrin
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The prophet's bibliography is considered as one of the oldest Islamic literature works.
These works are historically significant for two reasons. On the first hand, they are seen as
manuscripts documenting the life of the Holy Prophet Muhammad (pea
ce is upon him). On
the other hand, they predict for the Arab thought methodology in the outsets of entering the
field of writing and methodological authorship.
This research focuses on the concepts of the prophet's bibliography, the reasons
behind writing it, its resources and its harbingers. Moreover, it studies the content of five
books exploring the writing methodology in them. These books, which are considered as
the first stage of this type of writings, have been chosen in an effort to gain access to the
results that show the authorship methodology in the case of the pioneers. In addition, this
research highlights their role in establishing for writing in other types.
Authorship attribution is the task of assigning an unknown document to an author from a set of candidates. In the past, studies in this field use various evaluation datasets to demonstrate the effectiveness of preprocessing steps, features, and model
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