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Register a new userA Call for Clarity in Contemporary Authorship Attribution Evaluation
دعوة لوضوح في تقييم إسناد التأليف المعاصر
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وثقت البحوث الحديثة أن النتائج التي تم الإبلاغ عنها في أوراق إسناد التأليف المتأتلة بشكل متكرر يصعب إنتاجها.غالبا ما يقترح الكود والبيانات التي يتعذر الوصول إليها كعوامل تمنع النسخ الناجحة.حتى عندما تتوفر المواد الأصلية، تظل المشكلات التي تمنع الباحثين من مقارنة فعالية طرق مختلفة.لحل المشاكل المتبقية --- عدم وجود مجموعات اختبار ثابت واستخدام كورسا متجانسة بشكل غير لائق --- ورقة لدينا تساهم مواد لخمس تجارب تحديد الهوية المؤقتة المغلقة.تتميز التجارب الخمس بنصوص من 106 مؤلفة متميزة.تشمل التجارب مجموعة من النثر الإنجليزي الأمريكي الأمريكي المعاصر.توفر هذه التجارب الأساس لأبحاث إسناد التأليف المشبعة والمؤثرات القابلة للتكرار التي تنطوي على كتابة معاصرة.
Recent research has documented that results reported in frequently-cited authorship attribution papers are difficult to reproduce. Inaccessible code and data are often proposed as factors which block successful reproductions. Even when original materials 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.
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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
s. However, only a small fraction of works use more than one dataset to prove claims. In this paper, we present a collection of highly diverse authorship attribution datasets, which better generalizes evaluation results from authorship attribution research. Furthermore, we implement a wide variety of previously used machine learning models and show that many approaches show vastly different performances when applied to different datasets. We include pre-trained language models, for the first time testing them in this field in a systematic way. Finally, we propose a set of aggregated scores to evaluate different aspects of the dataset collection.
Cross-language authorship attribution is the challenging task of classifying documents by bilingual authors where the training documents are written in a different language than the evaluation documents. Traditional solutions rely on either translati
on to enable the use of single-language features, or language-independent feature extraction methods. More recently, transformer-based language models like BERT can also be pre-trained on multiple languages, making them intuitive candidates for cross-language classifiers which have not been used for this task yet. We perform extensive experiments to benchmark the performance of three different approaches to a smallscale cross-language authorship attribution experiment: (1) using language-independent features with traditional classification models, (2) using multilingual pre-trained language models, and (3) using machine translation to allow single-language classification. For the language-independent features, we utilize universal syntactic features like part-of-speech tags and dependency graphs, and multilingual BERT as a pre-trained language model. We use a small-scale social media comments dataset, closely reflecting practical scenarios. We show that applying machine translation drastically increases the performance of almost all approaches, and that the syntactic features in combination with the translation step achieve the best overall classification performance. In particular, we demonstrate that pre-trained language models are outperformed by traditional models in small scale authorship attribution problems for every language combination analyzed in this paper.
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.
As NLP systems become better at detecting opinions and beliefs from text, it is important to ensure not only that models are accurate but also that they arrive at their predictions in ways that align with human reasoning. In this work, we present a m
ethod for imparting human-like rationalization to a stance detection model using crowdsourced annotations on a small fraction of the training data. We show that in a data-scarce setting, our approach can improve the reasoning of a state-of-the-art classifier---particularly for inputs containing challenging phenomena such as sarcasm---at no cost in predictive performance. Furthermore, we demonstrate that attention weights surpass a leading attribution method in providing faithful explanations of our model's predictions, thus serving as a computationally cheap and reliable source of attributions for our model.
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.
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