Siamese networks for large-scale author identification. (November 2021)

• Record Type: Journal Article
• Authors: Saedi, Chakaveh; Dras, Mark
• Is Part Of: Computer speech & language. Volume 70(2021)
• Publication Date: 2021-11
• Subjects: Author identification -- Text categorisation -- Siamese neural network; Speech processing systems -- Periodicals; Automatic speech recognition -- Periodicals; Computers -- Periodicals; Linguistics -- Periodicals; Speech-Language Pathology -- Periodicals; Traitement automatique de la parole -- Périodiques; Reconnaissance automatique de la parole -- Périodiques; Automatic speech recognition; Speech processing systems; Electronic journals; Periodicals; 006.454
• DOI: 10.1016/j.csl.2021.101241
• Languages: English
• ISSNs: 0885-2308

Abstract

Highlights: Deep learning Siamese architectures perform well on author identification. They can identify texts by previously unseen authors. There is an unexpected interaction between Siamese energy function and size of author candidate pool. Abstract: Authorship attribution is the process of identifying the author of a text. Approaches to tackling it have been conventionally divided into classification-based ones, which work well for small numbers of candidate authors, and similarity-based methods, which are applicable for larger numbers of authors or for authors beyond the training set; these existing similarity-based methods have only embodied static notions of similarity. Deep learning methods, which blur the boundaries between classification-based and similarity-based approaches, are promising in terms of ability to learn a notion of similarity, but have previously only been used in a conventional small-closed-class classification setup. Siamese networks have been used to develop learned notions of similarity in one-shot image tasks, and also for tasks of mostly semantic relatedness in NLP. We examine their application to the stylistic task of authorship attribution on datasets with large numbers of authors, looking at multiple energy functions and neural network architectures, and show that they can substantially outperform previous approaches.